Class Vector<E>
 Type Parameters:
E
 the boxed version ofETYPE
, the element type of a vector
 Direct Known Subclasses:
ByteVector
,DoubleVector
,FloatVector
,IntVector
,LongVector
,ShortVector
byte
, long
, or float
.
Each lane contains an independent value of the element type.
Operations on vectors are typically
lanewise,
distributing some scalar operator (such as
addition)
across the lanes of the participating vectors,
usually generating a vector result whose lanes contain the various
scalar results. When run on a supporting platform, lanewise
operations can be executed in parallel by the hardware. This style
of parallelism is called Single Instruction Multiple Data
(SIMD) parallelism.
In the SIMD style of programming, most of the operations within
a vector lane are unconditional, but the effect of conditional
execution may be achieved using
masked operations
such as blend()
,
under the control of an associated VectorMask
.
Data motion other than strictly lanewise flow is achieved using
crosslane
operations, often under the control of an associated
VectorShuffle
.
Lane data and/or whole vectors can be reformatted using various
kinds of lanewise
conversions,
and bytewise reformatting
reinterpretations,
often under the control of a reflective VectorSpecies
object which selects an alternative vector format different
from that of the input vector.
Vector<E>
declares a set of vector operations (methods)
that are common to all element types. These common operations
include generic access to lane values, data selection and movement,
reformatting, and certain arithmetic and logical operations (such as addition
or comparison) that are common to all primitive types.
Public subtypes of Vector
correspond to specific
element types. These declare further operations that are specific
to that element type, including unboxed access to lane values,
bitwise operations on values of integral element types, or
transcendental operations on values of floating point element
types.
Some lanewise operations, such as the add
operator, are defined as
a fullservice named operation, where a corresponding method on Vector
comes in masked and unmasked overloadings, and (in subclasses) also comes in
covariant overrides (returning the subclass) and additional scalarbroadcast
overloadings (both masked and unmasked).
Other lanewise operations, such as the min
operator, are defined as a
partially serviced (not a fullservice) named operation, where a corresponding
method on Vector
and/or a subclass provide some but all possible
overloadings and overrides (commonly the unmasked variant with scalarbroadcast
overloadings).
Finally, all lanewise operations (those named as previously described,
or otherwise unnamed methodwise) have a corresponding
operator token
declared as a static constant on VectorOperators
.
Each operator token defines a symbolic Java expression for the operation,
such as a + b
for the
ADD
operator token.
General lanewise operationtoken accepting methods, such as for a
unary lanewise
operation, are provided on Vector
and come in the same variants as
a fullservice named operation.
This package contains a public subtype of Vector
corresponding to each supported element type:
ByteVector
, ShortVector
,
IntVector
, LongVector
,
FloatVector
, and DoubleVector
.
The element type of a vector,
referred to as ETYPE
, is one of the primitive types
byte
, short
, int
, long
,
float
, or double
.
The type E
in Vector<E>
is the boxed version
of ETYPE
. For example, in the type Vector<Integer>
, the E
parameter is Integer
and the ETYPE
is int
. In such a
vector, each lane carries a primitive int
value. This pattern continues
for the other primitive types as well. (See also sections 5.1.7 and
5.1.8 of the The Java Language Specification.)
The length of a vector
is the lane count, the number of lanes it contains.
This number is also called VLENGTH
when the context makes
clear which vector it belongs to. Each vector has its own fixed
VLENGTH
but different instances of vectors may have
different lengths. VLENGTH
is an important number, because
it estimates the SIMD performance gain of a single vector operation
as compared to scalar execution of the VLENGTH
scalar
operators which underly the vector operation.
Shapes and species
The information capacity of a vector is determined by its vector shape, also called itsVSHAPE
. Each possible VSHAPE
is represented by
a member of the VectorShape
enumeration, and represents
an implementation format shared in common by all vectors of
that shape. Thus, the size in bits of
of a vector is determined by appealing to its vector shape.
Some Java platforms give special support to only one shape, while others support several. A typical platform is not likely to support all the shapes described by this API. For this reason, most vector operations work on a single input shape and produce the same shape on output. Operations which change shape are clearly documented as such shapechanging, while the majority of operations are shapeinvariant, to avoid disadvantaging platforms which support only one shape. There are queries to discover, for the current Java platform, the preferred shape for general SIMD computation, or the largest available shape for any given lane type. To be portable, code using this API should start by querying a supported shape, and then process all data with shapeinvariant operations, within the selected shape.
Each unique combination of element type and vector shape
determines a unique
vector species.
A vector species is represented by a fixed instance of
VectorSpecies<E>
shared in common by all vectors of the same shape and
ETYPE
.
Unless otherwise documented, lanewise vector operations
require that all vector inputs have exactly the same VSHAPE
and VLENGTH
, which is to say that they must have exactly
the same species. This allows corresponding lanes to be paired
unambiguously. The check()
method
provides an easy way to perform this check explicitly.
Vector shape, VLENGTH
, and ETYPE
are all
mutually constrained, so that VLENGTH
times the
bitsize of each lane
must always match the bitsize of the vector's shape.
Thus, reinterpreting a
vector may double its length if and only if it either halves the lane size,
or else changes the shape. Likewise, reinterpreting a vector may double the
lane size if and only if it either halves the length, or else changes the
shape of the vector.
Vector subtypes
Vector declares a set of vector operations (methods) that are common to all element types (such as addition). Subclasses of Vector with a concrete element type declare further operations that are specific to that element type (such as access to element values in lanes, logical operations on values of integral elements types, or transcendental operations on values of floating point element types). There are six abstract subclasses of Vector corresponding to the supported set of element types,ByteVector
, ShortVector
,
IntVector
, LongVector
, FloatVector
, and
DoubleVector
. Along with typespecific operations these classes
support creation of vector values (instances of Vector).
They expose static constants corresponding to the supported species,
and static methods on these types generally take a species as a parameter.
For example,
FloatVector.fromArray
creates and returns a float vector of the specified species, with elements
loaded from the specified float array.
It is recommended that Species instances be held in static final
fields for optimal creation and usage of Vector values by the runtime compiler.
As an example of static constants defined by the typed vector classes,
constant FloatVector.SPECIES_256
is the unique species whose lanes are float
s and whose
vector size is 256 bits. Again, the constant
FloatVector.SPECIES_PREFERRED
is the species which
best supports processing of float
vector lanes on
the currently running Java platform.
As another example, a broadcast scalar value of
(double)0.5
can be obtained by calling
DoubleVector.broadcast(dsp, 0.5)
, but the argument dsp
is
required to select the species (and hence the shape and length) of
the resulting vector.
Lanewise operations
We use the term lanes when defining operations on vectors. The number of lanes in a vector is the number of scalar elements it holds. For example, a vector of typefloat
and
shape S_256_BIT
has eight lanes, since 32*8=256
.
Most operations on vectors are lanewise, which means the operation
is composed of an underlying scalar operator, which is repeated for
each distinct lane of the input vector. If there are additional
vector arguments of the same type, their lanes are aligned with the
lanes of the first input vector. (They must all have a common
VLENGTH
.) For most lanewise operations, the output resulting
from a lanewise operation will have a VLENGTH
which is equal to
the VLENGTH
of the input(s) to the operation. Thus, such lanewise
operations are lengthinvariant, in their basic definitions.
The principle of lengthinvariance is combined with another
basic principle, that most lengthinvariant lanewise operations are also
shapeinvariant, meaning that the inputs and the output of
a lanewise operation will have a common VSHAPE
. When the
principles conflict, because a logical result (with an invariant
VLENGTH
), does not fit into the invariant VSHAPE
,
the resulting expansions and contractions are handled explicitly
with
special conventions.
Vector operations can be grouped into various categories and
their behavior can be generally specified in terms of underlying
scalar operators. In the examples below, ETYPE
is the
element type of the operation (such as int.class
) and
EVector
is the corresponding concrete vector type (such as
IntVector.class
).

A lanewise unary operation, such as
w = v0.
neg
()
, takes one input vector, distributing a unary scalar operator across the lanes, and produces a result vector of the same type and shape. For each lane of the input vectora
, the underlying scalar operator is applied to the lane value. The result is placed into the vector result in the same lane. The following pseudocode illustrates the behavior of this operation category:ETYPE scalar_unary_op(ETYPE s); EVector a = ...; VectorSpecies<E> species = a.species(); ETYPE[] ar = new ETYPE[a.length()]; for (int i = 0; i < ar.length; i++) { ar[i] = scalar_unary_op(a.lane(i)); } EVector r = EVector.fromArray(species, ar, 0);

A lanewise binary operation, such as
w = v0.
add
(v1)
, takes two input vectors, distributing a binary scalar operator across the lanes, and produces a result vector of the same type and shape. For each lane of the two input vectorsa
andb
, the underlying scalar operator is applied to the lane values. The result is placed into the vector result in the same lane. The following pseudocode illustrates the behavior of this operation category:ETYPE scalar_binary_op(ETYPE s, ETYPE t); EVector a = ...; VectorSpecies<E> species = a.species(); EVector b = ...; b.check(species); // must have same species ETYPE[] ar = new ETYPE[a.length()]; for (int i = 0; i < ar.length; i++) { ar[i] = scalar_binary_op(a.lane(i), b.lane(i)); } EVector r = EVector.fromArray(species, ar, 0);

Generalizing from unary and binary operations,
a lanewise nary operation takes
N
input vectorsv[j]
, distributing an nary scalar operator across the lanes, and produces a result vector of the same type and shape. Except for a few ternary operations, such asw = v0.
fma
(v1,v2)
, this API has no support for lanewise nary operations. For each lane of all of the input vectorsv[j]
, the underlying scalar operator is applied to the lane values. The result is placed into the vector result in the same lane. The following pseudocode illustrates the behavior of this operation category:ETYPE scalar_nary_op(ETYPE... args); EVector[] v = ...; int N = v.length; VectorSpecies<E> species = v[0].species(); for (EVector arg : v) { arg.check(species); // all must have same species } ETYPE[] ar = new ETYPE[a.length()]; for (int i = 0; i < ar.length; i++) { ETYPE[] args = new ETYPE[N]; for (int j = 0; j < N; j++) { args[j] = v[j].lane(i); } ar[i] = scalar_nary_op(args); } EVector r = EVector.fromArray(species, ar, 0);

A lanewise conversion operation, such as
w0 = v0.
convert
(VectorOperators.I2D, 0)
, takes one input vector, distributing a unary scalar conversion operator across the lanes, and produces a logical result of the converted values. The logical result (or at least a part of it) is presented in a vector of the same shape as the input vector.Unlike other lanewise operations, conversions can change lane type, from the input (domain) type to the output (range) type. The lane size may change along with the type. In order to manage the size changes, lanewise conversion methods can product partial results, under the control of a
part
parameter, which is explained elsewhere. (Following the example above, the second group of converted lane values could be obtained asw1 = v0.convert(VectorOperators.I2D, 1)
.)The following pseudocode illustrates the behavior of this operation category in the specific example of a conversion from
int
todouble
, retaining either lower or upper lanes (depending onpart
) to maintain shapeinvariance:IntVector a = ...; int VLENGTH = a.length(); int part = ...; // 0 or 1 VectorShape VSHAPE = a.shape(); double[] arlogical = new double[VLENGTH]; for (int i = 0; i < limit; i++) { int e = a.lane(i); arlogical[i] = (double) e; } VectorSpecies<Double> rs = VSHAPE.withLanes(double.class); int M = Double.BITS / Integer.BITS; // expansion factor int offset = part * (VLENGTH / M); DoubleVector r = DoubleVector.fromArray(rs, arlogical, offset); assert r.length() == VLENGTH / M;

A crosslane reduction operation, such as
e = v0.
reduceLanes
(VectorOperators.ADD)
, operates on all the lane elements of an input vector. An accumulation function is applied to all the lane elements to produce a scalar result. If the reduction operation is associative then the result may be accumulated by operating on the lane elements in any order using a specified associative scalar binary operation and identity value. Otherwise, the reduction operation specifies the order of accumulation. The following pseudocode illustrates the behavior of this operation category if it is associative:ETYPE assoc_scalar_binary_op(ETYPE s, ETYPE t); EVector a = ...; ETYPE r = <identity value>; for (int i = 0; i < a.length(); i++) { r = assoc_scalar_binary_op(r, a.lane(i)); }

A crosslane movement operation, such as
w = v0.
rearrange
(shuffle)
operates on all the lane elements of an input vector and moves them in a datadependent manner into different lanes in an output vector. The movement is steered by an auxiliary datum, such as aVectorShuffle
or a scalar index defining the origin of the movement. The following pseudocode illustrates the behavior of this operation category, in the case of a shuffle:EVector a = ...; Shuffle<E> s = ...; ETYPE[] ar = new ETYPE[a.length()]; for (int i = 0; i < ar.length; i++) { int source = s.laneSource(i); ar[i] = a.lane(source); } EVector r = EVector.fromArray(a.species(), ar, 0);

A masked operation is one which is a variation on one of the
previous operations (either lanewise or crosslane), where
the operation takes an extra trailing
VectorMask
argument. In lanes the mask is set, the operation behaves as if the mask argument were absent, but in lanes where the mask is unset, the underlying scalar operation is suppressed. Masked operations are explained in greater detail elsewhere. 
A very special case of a masked lanewise binary operation is a
blend, which operates
lanewise on two input vectors
a
andb
, selecting lane values from one input or the other depending on a maskm
. In lanes wherem
is set, the corresponding value fromb
is selected into the result; otherwise the value froma
is selected. Thus, a blend acts as a vectorized version of Java's ternary selection expressionm?b:a
:ETYPE[] ar = new ETYPE[a.length()]; for (int i = 0; i < ar.length; i++) { boolean isSet = m.laneIsSet(i); ar[i] = isSet ? b.lane(i) : a.lane(i); } EVector r = EVector.fromArray(species, ar, 0);

A lanewise binary test operation, such as
m = v0.
lt
(v1)
, takes two input vectors, distributing a binary scalar comparison across the lanes, and produces, not a vector of booleans, but rather a vector mask. For each lane of the two input vectorsa
andb
, the underlying scalar comparison operator is applied to the lane values. The resulting boolean is placed into the vector mask result in the same lane. The following pseudocode illustrates the behavior of this operation category:boolean scalar_binary_test_op(ETYPE s, ETYPE t); EVector a = ...; VectorSpecies<E> species = a.species(); EVector b = ...; b.check(species); // must have same species boolean[] mr = new boolean[a.length()]; for (int i = 0; i < mr.length; i++) { mr[i] = scalar_binary_test_op(a.lane(i), b.lane(i)); } VectorMask<E> m = VectorMask.fromArray(species, mr, 0);

Similarly to a binary comparison, a lanewise unary test
operation, such as
m = v0.
test
(IS_FINITE)
, takes one input vector, distributing a scalar predicate (a test function) across the lanes, and produces a vector mask.
If a vector operation does not belong to one of the above categories then the method documentation explicitly specifies how it processes the lanes of input vectors, and where appropriate illustrates the behavior using pseudocode.
Most lanewise binary and comparison operations offer convenience
overloadings which accept a scalar as the second input, in place of a
vector. In this case the scalar value is promoted to a vector by
broadcasting it
into the same lane structure as the first input.
For example, to multiply all lanes of a double
vector by
a scalar value 1.1
, the expression v.mul(1.1)
is
easier to work with than an equivalent expression with an explicit
broadcast operation, such as v.mul(v.broadcast(1.1))
or v.mul(DoubleVector.broadcast(v.species(), 1.1))
.
Unless otherwise specified the scalar variant always behaves as if
each scalar value is first transformed to a vector of the same
species as the first vector input, using the appropriate
broadcast
operation.
Masked operations
Many vector operations accept an optional
mask
argument, selecting which lanes participate
in the underlying scalar operator. If present, the mask argument
appears at the end of the method argument list.
Each lane of the mask argument is a boolean which is either in the set or unset state. For lanes where the mask argument is unset, the underlying scalar operator is suppressed. In this way, masks allow vector operations to emulate scalar control flow operations, without losing SIMD parallelism, except where the mask lane is unset.
An operation suppressed by a mask will never cause an exception or side effect of any sort, even if the underlying scalar operator can potentially do so. For example, an unset lane that seems to access an out of bounds array element or divide an integral value by zero will simply be ignored. Values in suppressed lanes never participate or appear in the result of the overall operation.
Result lanes corresponding to a suppressed operation will be filled with a default value which depends on the specific operation, as follows:
 If the masked operation is a unary, binary, or nary arithmetic or logical operation, suppressed lanes are filled from the first vector operand (i.e., the vector receiving the method call), as if by a blend.
 If the masked operation is a memory load or a
slice()
from another vector, suppressed lanes are not loaded, and are filled with the default value for theETYPE
, which in every case consists of all zero bits. An unset lane can never cause an exception, even if the hypothetical corresponding memory location does not exist (because it is out of an array's index range).  If the operation is a crosslane operation with an operand
which supplies lane indexes (of type
VectorShuffle
orVector
, suppressed lanes are not computed, and are filled with the zero default value. Normally, invalid lane indexes elicit anIndexOutOfBoundsException
, but if a lane is unset, the zero value is quietly substituted, regardless of the index. This rule is similar to the previous rule, for masked memory loads.  If the masked operation is a memory store or an
unslice()
into another vector, suppressed lanes are not stored, and the corresponding memory or vector locations (if any) are unchanged.(Note: Memory effects such as race conditions never occur for suppressed lanes. That is, implementations will not secretly rewrite the existing value for unset lanes. In the Java Memory Model, reassigning a memory variable to its current value is not a noop; it may quietly undo a racing store from another thread.)
 If the masked operation is a reduction, suppressed lanes are ignored in the reduction. If all lanes are suppressed, a suitable neutral value is returned, depending on the specific reduction operation, and documented by the masked variant of that method. (This means that users can obtain the neutral value programmatically by executing the reduction on a dummy vector with an allunset mask.)
 If the masked operation is a comparison operation, suppressed output
lanes in the resulting mask are themselves unset, as if the
suppressed comparison operation returned
false
regardless of the suppressed input values. In effect, it is as if the comparison operation were performed unmasked, and then the result intersected with the controlling mask.  In other cases, such as masked crosslane movements, the specific effects of masking are documented by the masked variant of the method.
As an example, a masked binary operation on two input vectors
a
and b
suppresses the binary operation for lanes
where the mask is unset, and retains the original lane value from
a
. The following pseudocode illustrates this behavior:
ETYPE scalar_binary_op(ETYPE s, ETYPE t);
EVector a = ...;
VectorSpecies<E> species = a.species();
EVector b = ...;
b.check(species); // must have same species
VectorMask<E> m = ...;
m.check(species); // must have same species
boolean[] ar = new boolean[a.length()];
for (int i = 0; i < ar.length; i++) {
if (m.laneIsSet(i)) {
ar[i] = scalar_binary_op(a.lane(i), b.lane(i));
} else {
ar[i] = a.lane(i); // from first input
}
}
EVector r = EVector.fromArray(species, ar, 0);
Lane order and byte order
The number of lane values stored in a given vector is referred to as its vector length orVLENGTH
.
It is useful to consider vector lanes as ordered
sequentially from first to last, with the first lane
numbered 0
, the next lane numbered 1
, and so on to
the last lane numbered VLENGTH1
. This is a temporal
order, where lowernumbered lanes are considered earlier than
highernumbered (later) lanes. This API uses these terms
in preference to spatial terms such as "left", "right", "high",
and "low".
Temporal terminology works well for vectors because they (usually) represent small fixedsized segments in a long sequence of workload elements, where the workload is conceptually traversed in time order from beginning to end. (This is a mental model: it does not exclude multicore divideandconquer techniques.) Thus, when a scalar loop is transformed into a vector loop, adjacent scalar items (one earlier, one later) in the workload end up as adjacent lanes in a single vector (again, one earlier, one later). At a vector boundary, the last lane item in the earlier vector is adjacent to (and just before) the first lane item in the immediately following vector.
Vectors are also sometimes thought of in spatial terms, where the first lane is placed at an edge of some virtual paper, and subsequent lanes are presented in order next to it. When using spatial terms, all directions are equally plausible: Some vector notations present lanes from left to right, and others from right to left; still others present from top to bottom or vice versa. Using the language of time (before, after, first, last) instead of space (left, right, high, low) is often more likely to avoid misunderstandings.
As second reason to prefer temporal to spatial language about vector lanes is the fact that the terms "left", "right", "high" and "low" are widely used to describe the relations between bits in scalar values. The leftmost or highest bit in a given type is likely to be a sign bit, while the rightmost or lowest bit is likely to be the arithmetically least significant, and so on. Applying these terms to vector lanes risks confusion, however, because it is relatively rare to find algorithms where, given two adjacent vector lanes, one lane is somehow more arithmetically significant than its neighbor, and even in those cases, there is no general way to know which neighbor is the more significant.
Putting the terms together, we view the information structure of a vector as a temporal sequence of lanes ("first", "next", "earlier", "later", "last", etc.) of bitstrings which are internally ordered spatially (either "low" to "high" or "right" to "left"). The primitive values in the lanes are decoded from these bitstrings, in the usual way. Most vector operations, like most Java scalar operators, treat primitive values as atomic values, but some operations reveal the internal bitstring structure.
When a vector is loaded from or stored into memory, the order of vector lanes is always consistent with the inherent ordering of the memory container. This is true whether or not individual lane elements are subject to "byte swapping" due to details of byte order. Thus, while the scalar lane elements of vector might be "byte swapped", the lanes themselves are never reordered, except by an explicit method call that performs crosslane reordering.
When vector lane values are stored to Java variables of the same type, byte swapping is performed if and only if the implementation of the vector hardware requires such swapping. It is therefore unconditional and invisible.
As a useful fiction, this API presents a consistent illusion that vector lane bytes are composed into larger lane scalars in little endian order. This means that storing a vector into a Java byte array will reveal the successive bytes of the vector lane values in littleendian order on all platforms, regardless of native memory order, and also regardless of byte order (if any) within vector unit registers.
This hypothetical littleendian ordering also appears when a
reinterpretation cast is
applied in such a way that lane boundaries are discarded and
redrawn differently, while maintaining vector bits unchanged. In
such an operation, two adjacent lanes will contribute bytes to a
single new lane (or vice versa), and the sequential order of the
two lanes will determine the arithmetic order of the bytes in the
single lane. In this case, the littleendian convention provides
portable results, so that on all platforms earlier lanes tend to
contribute lower (rightward) bits, and later lanes tend to
contribute higher (leftward) bits. The reinterpretation casts between ByteVector
s and the
other nonbyte vectors use this convention to clarify their
portable semantics.
The littleendian fiction for relating lane order to perlane byte order is slightly preferable to an equivalent bigendian fiction, because some related formulas are much simpler, specifically those which renumber bytes after lane structure changes. The earliest byte is invariantly earliest across all lane structure changes, but only if littleendian convention are used. The root cause of this is that bytes in scalars are numbered from the least significant (rightmost) to the most significant (leftmost), and almost never viceversa. If we habitually numbered sign bits as zero (as on some computers) then this API would reach for bigendian fictions to create unified addressing of vector bytes.
Memory operations
As was already mentioned, vectors can be loaded from memory and stored back. An optional mask can control which individual memory locations are read from or written to. The shape of a vector determines how much memory it will occupy. An implementation typically has the property, in the absence of masking, that lanes are stored as a dense sequence of backtoback values in memory, the same as a dense (gapfree) series of single scalar values in an array of the scalar type. In such cases memory order corresponds exactly to lane order. The first vector lane value occupies the first position in memory, and so on, up to the length of the vector. Further, the memory order of stored vector lanes corresponds to increasing index values in a Java array or in aMemorySegment
.
Byte order for lane storage is chosen such that the stored vector values can be read or written as single primitive values, within the array or segment that holds the vector, producing the same values as the lanewise values within the vector. This fact is independent of the convenient fiction that lane values inside of vectors are stored in littleendian order.
For example,
FloatVector.fromArray(fsp,fa,i)
creates and returns a float vector of some particular species fsp
,
with elements loaded from some float array fa
.
The first lane is loaded from fa[i]
and the last lane
is initialized loaded from fa[i+VL1]
, where VL
is the length of the vector as derived from the species fsp
.
Then, fv=fv.add(fv2)
will produce another float vector of that species fsp
,
given a vector fv2
of the same species fsp
.
Next, mnz=fv.compare(NE, 0.0f)
tests whether the result is zero,
yielding a mask mnz
. The nonzero lanes (and only those
lanes) can then be stored back into the original array elements
using the statement
fv.intoArray(fa,i,mnz)
.
Expansions, contractions, and partial results
Since vectors are fixed in size, occasions often arise where the logical result of an operation is not the same as the physical size of the proposed output vector. To encourage user code that is as portable and predictable as possible, this API has a systematic approach to the design of such resizing vector operations. As a basic principle, lanewise operations are
lengthinvariant, unless clearly marked otherwise.
Lengthinvariance simply means that
if VLENGTH
lanes go into an operation, the same number
of lanes come out, with nothing discarded and no extra padding.
As a second principle, sometimes in tension with the first,
lanewise operations are also shapeinvariant, unless
clearly marked otherwise.
Shapeinvariance means that VSHAPE
is constant for typical
computations. Keeping the same shape throughout a computation
helps ensure that scarce vector resources are efficiently used.
(On some hardware platforms shape changes could cause unwanted
effects like extra data movement instructions, round trips through
memory, or pipeline bubbles.)
Tension between these principles arises when an operation
produces a logical result that is too large for the
required output VSHAPE
. In other cases, when a logical
result is smaller than the capacity of the output VSHAPE
,
the positioning of the logical result is open to question, since
the physical output vector must contain a mix of logical result and
padding.
In the first case, of a toolarge logical result being crammed
into a toosmall output VSHAPE
, we say that data has
expanded. In other words, an expansion operation
has caused the output shape to overflow. Symmetrically, in the
second case of a small logical result fitting into a roomy output
VSHAPE
, the data has contracted, and the
contraction operation has required the output shape to pad
itself with extra zero lanes.
In both cases we can speak of a parameter M
which
measures the expansion ratio or contraction ratio
between the logical result size (in bits) and the bitsize of the
actual output shape. When vector shapes are changed, and lane
sizes are not, M
is just the integral ratio of the output
shape to the logical result. (With the possible exception of
the maximum shape, all vector
sizes are powers of two, and so the ratio M
is always
an integer. In the hypothetical case of a nonintegral ratio,
the value M
would be rounded up to the next integer,
and then the same general considerations would apply.)
If the logical result is larger than the physical output shape,
such a shape change must inevitably drop result lanes (all but
1/M
of the logical result). If the logical size is smaller
than the output, the shape change must introduce zerofilled lanes
of padding (all but 1/M
of the physical output). The first
case, with dropped lanes, is an expansion, while the second, with
padding lanes added, is a contraction.
Similarly, consider a lanewise conversion operation which
leaves the shape invariant but changes the lane size by a ratio of
M
. If the logical result is larger than the output (or
input), this conversion must reduce the VLENGTH
lanes of the
output by M
, dropping all but 1/M
of the logical
result lanes. As before, the dropping of lanes is the hallmark of
an expansion. A lanewise operation which contracts lane size by a
ratio of M
must increase the VLENGTH
by the same
factor M
, filling the extra lanes with a zero padding
value; because padding must be added this is a contraction.
It is also possible (though somewhat confusing) to change both lane size and container size in one operation which performs both lane conversion and reshaping. If this is done, the same rules apply, but the logical result size is the product of the input size times any expansion or contraction ratio from the lane change size.
For completeness, we can also speak of inplace
operations for the frequent case when resizing does not occur.
With an inplace operation, the data is simply copied from logical
output to its physical container with no truncation or padding.
The ratio parameter M
in this case is unity.
Note that the classification of contraction vs. expansion
depends on the relative sizes of the logical result and the
physical output container. The size of the input container may be
larger or smaller than either of the other two values, without
changing the classification. For example, a conversion from a
128bit shape to a 256bit shape will be a contraction in many
cases, but it would be an expansion if it were combined with a
conversion from byte
to long
, since in that case
the logical result would be 1024 bits in size. This example also
illustrates that a logical result does not need to correspond to
any particular platformsupported vector shape.
Although lanewise masked operations can be viewed as producing partial operations, they are not classified (in this API) as expansions or contractions. A masked load from an array surely produces a partial vector, but there is no meaningful "logical output vector" that this partial result was contracted from.
Some care is required with these terms, because it is the data, not the container size, that is expanding or contracting, relative to the size of its output container. Thus, resizing a 128bit input into 512bit vector has the effect of a contraction. Though the 128 bits of payload hasn't changed in size, we can say it "looks smaller" in its new 512bit home, and this will capture the practical details of the situation.
If a vector method might expand its data, it accepts an extra
int
parameter called part
, or the "part number".
The part number must be in the range [0..M1]
, where
M
is the expansion ratio. The part number selects one
of M
contiguous disjoint equallysized blocks of lanes
from the logical result and fills the physical output vector
with this block of lanes.
Specifically, the lanes selected from the logical result of an
expansion are numbered in the range [R..R+L1]
, where
L
is the VLENGTH
of the physical output vector, and
the origin of the block, R
, is part*L
.
A similar convention applies to any vector method that might
contract its data. Such a method also accepts an extra part number
parameter (again called part
) which steers the contracted
data lanes one of M
contiguous disjoint equallysized
blocks of lanes in the physical output vector. The remaining lanes
are filled with zero, or as specified by the method.
Specifically, the data is steered into the lanes numbered in the
range [R..R+L1]
, where L
is the VLENGTH
of
the logical result vector, and the origin of the block, R
,
is again a multiple of L
selected by the part number,
specifically part*L
.
In the case of a contraction, the part number must be in the
nonpositive range [M+1..0]
. This convention is adopted
because some methods can perform both expansions and contractions,
in a datadependent manner, and the extra sign on the part number
serves as an error check. If vector method takes a part number and
is invoked to perform an inplace operation (neither contracting
nor expanding), the part
parameter must be exactly zero.
Part numbers outside the allowed ranges will elicit an indexing
exception. Note that in all cases a zero part number is valid, and
corresponds to an operation which preserves as many lanes as
possible from the beginning of the logical result, and places them
into the beginning of the physical output container. This is
often a desirable default, so a part number of zero is safe
in all cases and useful in most cases.
The various resizing operations of this API contract or expand their data as follows:

Vector.convert()
will expand (respectively, contract) its operand by ratioM
if the element size of its output is larger (respectively, smaller) by a factor ofM
. If the element sizes of input and output are the same, thenconvert()
is an inplace operation. 
Vector.convertShape()
will expand (respectively, contract) its operand by ratioM
if the bitsize of its logical result is larger (respectively, smaller) than the bitsize of its output shape. The size of the logical result is defined as the element size of the output, times theVLENGTH
of its input. Depending on the ratio of the changed lane sizes, the logical size may be (in various cases) either larger or smaller than the input vector, independently of whether the operation is an expansion or contraction. 
Since
Vector.castShape()
is a convenience method forconvertShape()
, its classification as an expansion or contraction is the same as forconvertShape()
. 
Vector.reinterpretShape()
is an expansion (respectively, contraction) by ratioM
if the vector bitsize of its input is crammed into a smaller (respectively, dropped into a larger) output container by a factor ofM
. Otherwise it is an inplace operation. Since this method is a reinterpretation cast that can erase and redraw lane boundaries as well as modify shape, the input vector's lane size and lane count are irrelevant to its classification as expanding or contracting. 
The
unslice()
methods expand by a ratio ofM=2
, because the single input slice is positioned and inserted somewhere within two consecutive background vectors. The part number selects the first or second background vector, as updated by the inserted slice. Note that the correspondingslice()
methods, although inverse to theunslice()
methods, do not contract their data and thus require no part number. This is becauseslice()
delivers a slice of exactlyVLENGTH
lanes extracted from two input vectors.
partLimit()
on VectorSpecies
can be used, before any
expanding or contracting operation is performed, to query the
limiting value on a part parameter for a proposed expansion
or contraction. The value returned from partLimit()
is
positive for expansions, negative for contractions, and zero for
inplace operations. Its absolute value is the parameter
M
, and so it serves as an exclusive limit on valid part number
arguments for the relevant methods. Thus, for expansions, the
partLimit()
value M
is the exclusive upper limit
for part numbers, while for contractions the partLimit()
value M
is the exclusive lower limit.
Moving data across lane boundaries
The crosslane methods which do not redraw lanes or change species are more regularly structured and easier to reason about. These operations are: The
slice()
family of methods, which extract contiguous slice ofVLENGTH
fields from a given origin point within a concatenated pair of vectors.  The
unslice()
family of methods, which insert a contiguous slice ofVLENGTH
fields into a concatenated pair of vectors at a given origin point.  The
rearrange()
family of methods, which select an arbitrary set ofVLENGTH
lanes from one or two input vectors, and assemble them in an arbitrary order. The selection and order of lanes is controlled by aVectorShuffle
object, which acts as an routing table mapping source lanes to destination lanes. AVectorShuffle
can encode a mathematical permutation as well as many other patterns of data movement.  The
compress(VectorMask)
andexpand(VectorMask)
methods, which select up toVLENGTH
lanes from an input vector, and assemble them in lane order. The selection of lanes is controlled by aVectorMask
, with set lane elements mapping, by compression or expansion in lane order, source lanes to destination lanes.
Some vector operations are not lanewise, but rather move data across lane boundaries. Such operations are typically rare in SIMD code, though they are sometimes necessary for specific algorithms that manipulate data formats at a low level, and/or require SIMD data to move in complex local patterns. (Local movement in a small window of a large array of data is relatively unusual, although some highly patterned algorithms call for it.) In this API such methods are always clearly recognizable, so that simpler lanewise reasoning can be confidently applied to the rest of the code.
In some cases, vector lane boundaries are discarded and
"redrawn from scratch", so that data in a given input lane might
appear (in several parts) distributed through several output lanes,
or (conversely) data from several input lanes might be consolidated
into a single output lane. The fundamental method which can redraw
lanes boundaries is
reinterpretShape()
.
Built on top of this method, certain convenience methods such
as reinterpretAsBytes()
or
reinterpretAsInts()
will
(potentially) redraw lane boundaries, while retaining the
same overall vector shape.
Operations which produce or consume a scalar result can be
viewed as very simple crosslane operations. Methods in the
reduceLanes()
family fold together all lanes (or maskselected
lanes) of a method and return a single result. As an inverse, the
broadcast
family of methods can be thought
of as crossing lanes in the other direction, from a scalar to all
lanes of the output vector. Singlelane access methods such as
lane(I)
or withLane(I,E)
might also be regarded as
very simple crosslane operations.
Likewise, a method which moves a nonbyte vector to or from a byte array could be viewed as a crosslane operation, because the vector lanes must be distributed into separate bytes, or (in the other direction) consolidated from array bytes.
 Implementation Note:
Hardware platform dependencies and limitations
The Vector API is to accelerate computations in style of Single Instruction Multiple Data (SIMD), using available hardware resources such as vector hardware registers and vector hardware instructions. The API is designed to make effective use of multiple SIMD hardware platforms.This API will also work correctly even on Java platforms which do not include specialized hardware support for SIMD computations. The Vector API is not likely to provide any special performance benefit on such platforms.
Currently the implementation is optimized to work best on:
 Intel x64 platforms supporting at least AVX2 up to AVX512. Masking using mask registers and mask accepting hardware instructions on AVX512 are not currently supported.
 ARM AArch64 platforms supporting NEON. Although the API has been designed to ensure ARM SVE instructions can be supported (vector sizes between 128 to 2048 bits) there is currently no implementation of such instructions and the general masking capability.
blend
as in the expressiona.blend(a.lanewise(op, b), m)
, wherea
andb
are vectors,op
is the vector operation, andm
is the mask.The implementation does not currently support optimal vectorized instructions for floating point transcendental functions (such as operators
SIN
andLOG
).No boxing of primitives
Although a vector type likeVector<Integer>
may seem to work with boxedInteger
values, the overheads associated with boxing are avoided by having each vector subtype work internally on lane values of the actualETYPE
, such asint
.Valuebased classes and identity operations
Vector
, along with all of its subtypes and many of its helper types likeVectorMask
andVectorShuffle
, is a valuebased class.Once created, a vector is never mutated, not even if only a single lane is changed. A new vector is always created to hold a new configuration of lane values. The unavailability of mutative methods is a necessary consequence of suppressing the object identity of all vectors, as valuebased classes.
With
Vector
, identitysensitive operations such as==
may yield unpredictable results, or reduced performance. Oddly enough,v.equals(w)
is likely to be faster thanv==w
, sinceequals
is not an identity sensitive method. Also, these objects can be stored in locals and parameters and asstatic final
constants, but storing them in other Java fields or in array elements, while semantically valid, may incur performance penalties.

Method Summary
Modifier and TypeMethodDescriptionabs()
Returns the absolute value of this vector.Adds this vector to a second input vector.add
(Vector<E> v, VectorMask<E> m) Adds this vector to a second input vector, selecting lanes under the control of a mask.addIndex
(int scale) Adds the lanes of this vector to their corresponding lane numbers, scaled by a given constant.abstract int
bitSize()
Returns the total size, in bits, of this vector.blend
(long e, VectorMask<E> m) Replaces selected lanes of this vector with a scalar value under the control of a mask.blend
(Vector<E> v, VectorMask<E> m) Replaces selected lanes of this vector with corresponding lanes from a second input vector under the control of a mask.broadcast
(long e) Returns a vector of the same species as this one where all lane elements are set to the primitive valuee
.abstract int
byteSize()
Returns the total size, in bytes, of this vector.abstract <F> Vector
<F> castShape
(VectorSpecies<F> rsp, int part) Convenience method for converting a vector from one lane type to another, reshaping as needed when lane sizes change.abstract <F> Vector
<F> Checks that this vector has the given element type, and returns this vector unchanged.abstract <F> Vector
<F> check
(VectorSpecies<F> species) Checks that this vector has the given species, and returns this vector unchanged.abstract VectorMask
<E> compare
(VectorOperators.Comparison op, long e) Tests this vector by comparing it with an input scalar, according to the given comparison operation.abstract VectorMask
<E> compare
(VectorOperators.Comparison op, long e, VectorMask<E> m) Tests this vector by comparing it with an input scalar, according to the given comparison operation, in lanes selected by a mask.abstract VectorMask
<E> compare
(VectorOperators.Comparison op, Vector<E> v) Tests this vector by comparing it with another input vector, according to the given comparison operation.abstract VectorMask
<E> compare
(VectorOperators.Comparison op, Vector<E> v, VectorMask<E> m) Tests this vector by comparing it with another input vector, according to the given comparison operation, in lanes selected by a mask.compress
(VectorMask<E> m) Compresses the lane elements of this vector selecting lanes under the control of a specific mask.abstract <F> Vector
<F> convert
(VectorOperators.Conversion<E, F> conv, int part) Convert this vector to a vector of the same shape and a new element type, converting lane values from the currentETYPE
to a new lane type (calledFTYPE
here) according to the indicated conversion.abstract <F> Vector
<F> convertShape
(VectorOperators.Conversion<E, F> conv, VectorSpecies<F> rsp, int part) Converts this vector to a vector of the given species, shape and element type, converting lane values from the currentETYPE
to a new lane type (calledFTYPE
here) according to the indicated conversion.Divides this vector by a second input vector.div
(Vector<E> v, VectorMask<E> m) Divides this vector by a second input vector under the control of a mask.abstract int
Returns the size of each lane, in bits, of this vector.Returns the primitive element type (ETYPE
) of this vector.abstract VectorMask
<E> Tests if this vector is equal to another input vector.abstract boolean
Indicates whether this vector is identical to some other object.expand
(VectorMask<E> m) Expands the lane elements of this vector under the control of a specific mask.protected final Object
abstract int
hashCode()
Returns a hash code value for the vector.abstract void
intoMemorySegment
(MemorySegment ms, long offset, ByteOrder bo) Stores this vector into a memory segment starting at an offset using explicit byte order.abstract void
intoMemorySegment
(MemorySegment ms, long offset, ByteOrder bo, VectorMask<E> m) Stores this vector into a memory segment starting at an offset using explicit byte order and a mask.lanewise
(VectorOperators.Binary op, long e) Combines the lane values of this vector with the value of a broadcast scalar.lanewise
(VectorOperators.Binary op, long e, VectorMask<E> m) Combines the corresponding lane values of this vector with those of a second input vector, with selection of lane elements controlled by a mask.lanewise
(VectorOperators.Binary op, Vector<E> v) Combines the corresponding lane values of this vector with those of a second input vector.lanewise
(VectorOperators.Binary op, Vector<E> v, VectorMask<E> m) Combines the corresponding lane values of this vector with those of a second input vector, with selection of lane elements controlled by a mask.Combines the corresponding lane values of this vector with the lanes of a second and a third input vector.lanewise
(VectorOperators.Ternary op, Vector<E> v1, Vector<E> v2, VectorMask<E> m) Combines the corresponding lane values of this vector with the lanes of a second and a third input vector, with selection of lane elements controlled by a mask.Operates on the lane values of this vector.lanewise
(VectorOperators.Unary op, VectorMask<E> m) Operates on the lane values of this vector, with selection of lane elements controlled by a mask.abstract int
length()
Returns the lane count, or vector length (VLENGTH
).abstract VectorMask
<E> Tests if this vector is less than another input vector.abstract VectorMask
<E> maskAll
(boolean bit) Returns a mask of same species as this vector, where each lane is set or unset according to given single boolean, which is broadcast to all lanes.Computes the larger of this vector and a second input vector.Computes the smaller of this vector and a second input vector.Multiplies this vector by a second input vector.mul
(Vector<E> v, VectorMask<E> m) Multiplies this vector by a second input vector under the control of a mask.neg()
Negates this vector.rearrange
(VectorShuffle<E> s) Rearranges the lane elements of this vector, selecting lanes under the control of a specific shuffle.rearrange
(VectorShuffle<E> s, Vector<E> v) Rearranges the lane elements of two vectors, selecting lanes under the control of a specific shuffle, using both normal and exceptional indexes in the shuffle to steer data.rearrange
(VectorShuffle<E> s, VectorMask<E> m) Rearranges the lane elements of this vector, selecting lanes under the control of a specific shuffle and a mask.abstract long
Returns a value accumulated from all the lanes of this vector.abstract long
Returns a value accumulated from selected lanes of this vector, controlled by a mask.abstract ByteVector
Views this vector as a vector of the same shape and contents but a lane type ofbyte
, where the bytes are extracted from the lanes according to littleendian order.abstract DoubleVector
Reinterprets this vector as a vector of the same shape and contents but a lane type ofdouble
, where the lanes are assembled from successive bytes according to littleendian order.abstract FloatVector
Reinterprets this vector as a vector of the same shape and contents but a lane type offloat
, where the lanes are assembled from successive bytes according to littleendian order.abstract IntVector
Reinterprets this vector as a vector of the same shape and contents but a lane type ofint
, where the lanes are assembled from successive bytes according to littleendian order.abstract LongVector
Reinterprets this vector as a vector of the same shape and contents but a lane type oflong
, where the lanes are assembled from successive bytes according to littleendian order.abstract ShortVector
Reinterprets this vector as a vector of the same shape and contents but a lane type ofshort
, where the lanes are assembled from successive bytes according to littleendian order.abstract <F> Vector
<F> reinterpretShape
(VectorSpecies<F> species, int part) Transforms this vector to a vector of the given species of element typeF
, reinterpreting the bytes of this vector without performing any value conversions.selectFrom
(Vector<E> v) Using index values stored in the lanes of this vector, assemble values stored in second vectorv
.selectFrom
(Vector<E> v1, Vector<E> v2) Using values stored in the lanes of this vector, assemble values stored in the second vectorv1
and third vectorv2
.selectFrom
(Vector<E> v, VectorMask<E> m) Using index values stored in the lanes of this vector, assemble values stored in second vector, under the control of a mask.abstract VectorShape
shape()
Returns the shape of this vector.slice
(int origin) Slices a segment of adjacent lanes, starting at a givenorigin
lane in the current vector.Slices a segment of adjacent lanes, starting at a givenorigin
lane in the current vector, and continuing (as needed) into an immediately following vector.slice
(int origin, Vector<E> v1, VectorMask<E> m) Slices a segment of adjacent lanes under the control of a mask, starting at a givenorigin
lane in the current vector, and continuing (as needed) into an immediately following vector.abstract VectorSpecies
<E> species()
Returns the species of this vector.Subtracts a second input vector from this vector.sub
(Vector<E> v, VectorMask<E> m) Subtracts a second input vector from this vector under the control of a mask.abstract VectorMask
<E> Tests the lanes of this vector according to the given operation.abstract VectorMask
<E> test
(VectorOperators.Test op, VectorMask<E> m) Test selected lanes of this vector, according to the given operation.abstract Object
toArray()
Returns a packed array containing all the lane values.abstract double[]
Returns adouble[]
array containing all the lane values, converted to the typedouble
.abstract int[]
Returns anint[]
array containing all the lane values, converted to the typeint
.abstract long[]
Returns along[]
array containing all the lane values, converted to the typelong
.abstract VectorShuffle
<E> Converts this vector into a shuffle, converting the lane values toint
and regarding them as source indexes.abstract String
toString()
Returns a string representation of this vector, of the form"[0,1,2...]"
, reporting the lane values of this vector, in lane order.unslice
(int origin) Reverses a slice(), inserting the current vector as a slice within a "background" input of zero lane values.Reverses a slice(), inserting the current vector as a slice within another "background" input vector, which is regarded as one or the other input to a hypothetical subsequentslice()
operation.unslice
(int origin, Vector<E> w, int part, VectorMask<E> m) Reverses a slice(), inserting (under the control of a mask) the current vector as a slice within another "background" input vector, which is regarded as one or the other input to a hypothetical subsequentslice()
operation.abstract Vector
<?> Views this vector as a vector of the same shape, length, and contents, but a lane type that is a floatingpoint type.abstract Vector
<?> Views this vector as a vector of the same shape, length, and contents, but a lane type that is not a floatingpoint type.

Method Details

species
Returns the species of this vector. Returns:
 the species of this vector

elementType
Returns the primitive element type (ETYPE
) of this vector. Implementation Requirements:
 This is the same value as
this.species().elementType()
.  Returns:
 the primitive element type of this vector

elementSize
public abstract int elementSize()Returns the size of each lane, in bits, of this vector. Implementation Requirements:
 This is the same value as
this.species().elementSize()
.  Returns:
 the lane size, in bits, of this vector

shape
Returns the shape of this vector. Implementation Requirements:
 This is the same value as
this.species().vectorShape()
.  Returns:
 the shape of this vector

length
public abstract int length()Returns the lane count, or vector length (VLENGTH
). Returns:
 the lane count

bitSize
public abstract int bitSize()Returns the total size, in bits, of this vector. Implementation Requirements:
 This is the same value as
this.shape().vectorBitSize()
.  Returns:
 the total size, in bits, of this vector

byteSize
public abstract int byteSize()Returns the total size, in bytes, of this vector. Implementation Requirements:
 This is the same value as
this.bitSize()/Byte.SIZE
.  Returns:
 the total size, in bytes, of this vector

lanewise
Operates on the lane values of this vector. This is a lanewise unary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to process lane values Returns:
 the result of applying the operation lanewise to the input vector
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

lanewise
Operates on the lane values of this vector, with selection of lane elements controlled by a mask. This is a lanewise unary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to process lane valuesm
 the mask controlling lane selection Returns:
 the result of applying the operation lanewise to the input vector
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

lanewise
Combines the corresponding lane values of this vector with those of a second input vector. This is a lanewise binary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to combine lane valuesv
 the input vector Returns:
 the result of applying the operation lanewise to the two input vectors
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

lanewise
Combines the corresponding lane values of this vector with those of a second input vector, with selection of lane elements controlled by a mask. This is a lanewise binary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to combine lane valuesv
 the second input vectorm
 the mask controlling lane selection Returns:
 the result of applying the operation lanewise to the two input vectors
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

lanewise
Combines the lane values of this vector with the value of a broadcast scalar. This is a lanewise binary operation which applies the selected operation to each lane. The return value will be equal to this expression:this.lanewise(op, this.broadcast(e))
. API Note:
 The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. This rule is enforced by the implicit call tobroadcast()
.Subtypes improve on this method by sharpening the method return type and the type of the scalar parameter
e
.  Parameters:
op
 the operation used to combine lane valuese
 the input scalar Returns:
 the result of applying the operation lanewise to the input vector and the scalar
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operationIllegalArgumentException
 if the givenlong
value cannot be represented by the right operand type of the vector operation See Also:

lanewise
Combines the corresponding lane values of this vector with those of a second input vector, with selection of lane elements controlled by a mask. This is a lanewise binary operation which applies the selected operation to each lane. The second operand is a broadcast integral value. The return value will be equal to this expression:this.lanewise(op, this.broadcast(e), m)
. API Note:
 The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. This rule is enforced by the implicit call tobroadcast()
.Subtypes improve on this method by sharpening the method return type and the type of the scalar parameter
e
.  Parameters:
op
 the operation used to combine lane valuese
 the input scalarm
 the mask controlling lane selection Returns:
 the result of applying the operation lanewise to the input vector and the scalar
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operationIllegalArgumentException
 if the givenlong
value cannot be represented by the right operand type of the vector operation See Also:

lanewise
Combines the corresponding lane values of this vector with the lanes of a second and a third input vector. This is a lanewise ternary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to combine lane valuesv1
 the second input vectorv2
 the third input vector Returns:
 the result of applying the operation lanewise to the three input vectors
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

lanewise
public abstract Vector<E> lanewise(VectorOperators.Ternary op, Vector<E> v1, Vector<E> v2, VectorMask<E> m) Combines the corresponding lane values of this vector with the lanes of a second and a third input vector, with selection of lane elements controlled by a mask. This is a lanewise ternary operation which applies the selected operation to each lane. API Note:
 Subtypes improve on this method by sharpening the method return type.
 Parameters:
op
 the operation used to combine lane valuesv1
 the second input vectorv2
 the third input vectorm
 the mask controlling lane selection Returns:
 the result of applying the operation lanewise to the three input vectors
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

add
Adds this vector to a second input vector. This is a lanewise binary operation which applies the primitive addition operation (+
) to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
ADD
, v)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 a second input vector Returns:
 the result of adding this vector to the second input vector
 See Also:

add
Adds this vector to a second input vector, selecting lanes under the control of a mask. This is a masked lanewise binary operation which applies the primitive addition operation (+
) to each pair of corresponding lane values. For any lane unset in the mask, the primitive operation is suppressed and this vector retains the original value stored in that lane. This method is also equivalent to the expressionlanewise
(
ADD
, v, m)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 the second input vectorm
 the mask controlling lane selection Returns:
 the result of adding this vector to the given vector
 See Also:

sub
Subtracts a second input vector from this vector. This is a lanewise binary operation which applies the primitive subtraction operation (
) to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
SUB
, v)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 a second input vector Returns:
 the result of subtracting the second input vector from this vector
 See Also:

sub
Subtracts a second input vector from this vector under the control of a mask. This is a masked lanewise binary operation which applies the primitive subtraction operation (
) to each pair of corresponding lane values. For any lane unset in the mask, the primitive operation is suppressed and this vector retains the original value stored in that lane. This method is also equivalent to the expressionlanewise
(
SUB
, v, m)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 the second input vectorm
 the mask controlling lane selection Returns:
 the result of subtracting the second input vector from this vector
 See Also:

mul
Multiplies this vector by a second input vector. This is a lanewise binary operation which applies the primitive multiplication operation (*
) to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
MUL
, v)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 a second input vector Returns:
 the result of multiplying this vector by the second input vector
 See Also:

mul
Multiplies this vector by a second input vector under the control of a mask. This is a lanewise binary operation which applies the primitive multiplication operation (*
) to each pair of corresponding lane values. For any lane unset in the mask, the primitive operation is suppressed and this vector retains the original value stored in that lane. This method is also equivalent to the expressionlanewise
(
MUL
, v, m)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 Parameters:
v
 the second input vectorm
 the mask controlling lane selection Returns:
 the result of multiplying this vector by the given vector
 See Also:

div
Divides this vector by a second input vector. This is a lanewise binary operation which applies the primitive division operation (/
) to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
DIV
, v)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 API Note:
 If the underlying scalar operator does not support
division by zero, but is presented with a zero divisor,
an
ArithmeticException
will be thrown.  Parameters:
v
 a second input vector Returns:
 the result of dividing this vector by the second input vector
 Throws:
ArithmeticException
 if any lane inv
is zero andETYPE
is notfloat
ordouble
. See Also:

div
Divides this vector by a second input vector under the control of a mask. This is a lanewise binary operation which applies the primitive division operation (/
) to each pair of corresponding lane values. For any lane unset in the mask, the primitive operation is suppressed and this vector retains the original value stored in that lane. This method is also equivalent to the expressionlanewise
(
DIV
, v, m)
.As a fullservice named operation, this method comes in masked and unmasked overloadings, and (in subclasses) also comes in scalarbroadcast overloadings (both masked and unmasked).
 API Note:
 If the underlying scalar operator does not support
division by zero, but is presented with a zero divisor,
an
ArithmeticException
will be thrown.  Parameters:
v
 a second input vectorm
 the mask controlling lane selection Returns:
 the result of dividing this vector by the second input vector
 Throws:
ArithmeticException
 if any lane selected bym
inv
is zero andETYPE
is notfloat
ordouble
. See Also:

neg
Negates this vector. This is a lanewise unary operation which applies the primitive negation operation (x
) to each input lane. This method is also equivalent to the expressionlanewise
(
NEG
)
. API Note:
 This method has no masked variant, but the corresponding masked operation can be obtained from the lanewise method.
 Returns:
 the negation of this vector
 See Also:

abs
Returns the absolute value of this vector. This is a lanewise unary operation which applies the methodMath.abs
to each input lane. This method is also equivalent to the expressionlanewise
(
ABS
)
. API Note:
 This method has no masked variant, but the corresponding masked operation can be obtained from the lanewise method.
 Returns:
 the absolute value of this vector
 See Also:

min
Computes the smaller of this vector and a second input vector. This is a lanewise binary operation which applies the operationMath.min()
to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
MIN
, v)
. API Note:
 This is not a fullservice named operation like
add()
. A masked version of this operation is not directly available but may be obtained via the masked version oflanewise
. Subclasses define an additional scalarbroadcast overloading of this method.  Parameters:
v
 a second input vector Returns:
 the lanewise minimum of this vector and the second input vector
 See Also:

max
Computes the larger of this vector and a second input vector. This is a lanewise binary operation which applies the operationMath.max()
to each pair of corresponding lane values. This method is also equivalent to the expressionlanewise
(
MAX
, v)
.This is not a fullservice named operation like
add()
. A masked version of this operation is not directly available but may be obtained via the masked version oflanewise
. Subclasses define an additional scalarbroadcast overloading of this method. Parameters:
v
 a second input vector Returns:
 the lanewise maximum of this vector and the second input vector
 See Also:

reduceLanesToLong
Returns a value accumulated from all the lanes of this vector. This is an associative crosslane reduction operation which applies the specified operation to all the lane elements. The return value will be equal to this expression:(long) ((EVector)this).reduceLanes(op)
, whereEVector
is the vector class specific to this vector's element typeETYPE
.In the case of operations
ADD
andMUL
, whenETYPE
isfloat
ordouble
, the precise result, before casting, will reflect the choice of an arbitrary order of operations, which may even vary over time. For further details see the section Operations on floating point vectors. API Note:
 If the
ETYPE
isfloat
ordouble
, this operation can lose precision and/or range, as a normal part of casting the result down tolong
. Usually strongly typed access is preferable, if you are working with a vector subtype that has a known element type.  Parameters:
op
 the operation used to combine lane values Returns:
 the accumulated result, cast to
long
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

reduceLanesToLong
Returns a value accumulated from selected lanes of this vector, controlled by a mask. This is an associative crosslane reduction operation which applies the specified operation to the selected lane elements. The return value will be equal to this expression:(long) ((EVector)this).reduceLanes(op, m)
, whereEVector
is the vector class specific to this vector's element typeETYPE
.If no elements are selected, an operationspecific identity value is returned.

If the operation is
ADD
,XOR
, orOR
, then the identity value is zero. 
If the operation is
MUL
, then the identity value is one. 
If the operation is
AND
, then the identity value is minus one (all bits set). 
If the operation is
MAX
, then the identity value is theMIN_VALUE
of the vector's nativeETYPE
. (In the case of floating point types, the valueNEGATIVE_INFINITY
is used, and will appear after casting asLong.MIN_VALUE
. 
If the operation is
MIN
, then the identity value is theMAX_VALUE
of the vector's nativeETYPE
. (In the case of floating point types, the valuePOSITIVE_INFINITY
is used, and will appear after casting asLong.MAX_VALUE
.
In the case of operations
ADD
andMUL
, whenETYPE
isfloat
ordouble
, the precise result, before casting, will reflect the choice of an arbitrary order of operations, which may even vary over time. For further details see the section Operations on floating point vectors. API Note:
 If the
ETYPE
isfloat
ordouble
, this operation can lose precision and/or range, as a normal part of casting the result down tolong
. Usually strongly typed access is preferable, if you are working with a vector subtype that has a known element type.  Parameters:
op
 the operation used to combine lane valuesm
 the mask controlling lane selection Returns:
 the reduced result accumulated from the selected lane values
 Throws:
UnsupportedOperationException
 if this vector does not support the requested operation See Also:

If the operation is

test
Tests the lanes of this vector according to the given operation. This is a lanewise unary test operation which applies the given test operation to each lane value. Parameters:
op
 the operation used to test lane values Returns:
 the mask result of testing the lanes of this vector, according to the selected test operator
 See Also:

test
Test selected lanes of this vector, according to the given operation. This is a masked lanewise unary test operation which applies the given test operation to each lane value. The returned result is equal to the expressiontest(op).and(m)
. Parameters:
op
 the operation used to test lane valuesm
 the mask controlling lane selection Returns:
 the mask result of testing the lanes of this vector, according to the selected test operator, and only in the lanes selected by the mask
 See Also:

eq
Tests if this vector is equal to another input vector. This is a lanewise binary test operation which applies the primitive equals operation (==
) to each pair of corresponding lane values. The result is the same ascompare(VectorOperators.EQ, v)
. Parameters:
v
 a second input vector Returns:
 the mask result of testing lanewise if this vector equal to the second input vector
 See Also:

lt
Tests if this vector is less than another input vector. This is a lanewise binary test operation which applies the primitive lessthan operation (<
) to each lane. The result is the same ascompare(VectorOperators.LT, v)
. Parameters:
v
 a second input vector Returns:
 the mask result of testing lanewise if this vector is less than the second input vector
 See Also:

compare
Tests this vector by comparing it with another input vector, according to the given comparison operation. This is a lanewise binary test operation which applies the given comparison operation to each pair of corresponding lane values. Parameters:
op
 the operation used to compare lane valuesv
 a second input vector Returns:
 the mask result of testing lanewise if this vector compares to the input, according to the selected comparison operator
 See Also:

compare
Tests this vector by comparing it with another input vector, according to the given comparison operation, in lanes selected by a mask. This is a masked lanewise binary test operation which applies the given comparison operation to each pair of corresponding lane values. The returned result is equal to the expressioncompare(op,v).and(m)
. Parameters:
op
 the operation used to compare lane valuesv
 a second input vectorm
 the mask controlling lane selection Returns:
 the mask result of testing lanewise if this vector compares to the input, according to the selected comparison operator, and only in the lanes selected by the mask
 See Also:

compare
Tests this vector by comparing it with an input scalar, according to the given comparison operation. This is a lanewise binary test operation which applies the given comparison operation to each lane value, paired with the broadcast value.The result is the same as
this.compare(op, this.broadcast(e))
. That is, the scalar may be regarded as broadcast to a vector of the same species, and then compared against the original vector, using the selected comparison operation. API Note:
 The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. This rule is enforced by the implicit call tobroadcast()
.Subtypes improve on this method by sharpening the type of the scalar parameter
e
.  Parameters:
op
 the operation used to compare lane valuese
 the input scalar Returns:
 the mask result of testing lanewise if this vector compares to the input, according to the selected comparison operator
 Throws:
IllegalArgumentException
 if the givenlong
value cannot be represented by the vector'sETYPE
 See Also:

compare
Tests this vector by comparing it with an input scalar, according to the given comparison operation, in lanes selected by a mask. This is a masked lanewise binary test operation which applies the given comparison operation to each lane value, paired with the broadcast value. The returned result is equal to the expressioncompare(op,e).and(m)
. API Note:
 The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. This rule is enforced by the implicit call tobroadcast()
.Subtypes improve on this method by sharpening the type of the scalar parameter
e
.  Parameters:
op
 the operation used to compare lane valuese
 the input scalarm
 the mask controlling lane selection Returns:
 the mask result of testing lanewise if this vector compares to the input, according to the selected comparison operator, and only in the lanes selected by the mask
 Throws:
IllegalArgumentException
 if the givenlong
value cannot be represented by the vector'sETYPE
 See Also:

blend
Replaces selected lanes of this vector with corresponding lanes from a second input vector under the control of a mask. This is a masked lanewise binary operation which selects each lane value from one or the other input. For any lane set in the mask, the new lane value is taken from the second input vector, and replaces whatever value was in the that lane of this vector.
 For any lane unset in the mask, the replacement is suppressed and this vector retains the original value stored in that lane.
Vector<E> a = ...; VectorSpecies<E> species = a.species(); Vector<E> b = ...; b.check(species); VectorMask<E> m = ...; ETYPE[] ar = a.toArray(); for (int i = 0; i < ar.length; i++) { if (m.laneIsSet(i)) { ar[i] = b.lane(i); } } return EVector.fromArray(s, ar, 0);
 Parameters:
v
 the second input vector, containing replacement lane valuesm
 the mask controlling lane selection from the second input vector Returns:
 the result of blending the lane elements of this vector with those of the second input vector

blend
Replaces selected lanes of this vector with a scalar value under the control of a mask. This is a masked lanewise binary operation which selects each lane value from one or the other input. The returned result is equal to the expressionblend(broadcast(e),m)
. API Note:
 The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. This rule is enforced by the implicit call tobroadcast()
.Subtypes improve on this method by sharpening the type of the scalar parameter
e
.  Parameters:
e
 the input scalar, containing the replacement lane valuem
 the mask controlling lane selection of the scalar Returns:
 the result of blending the lane elements of this vector with the scalar value

addIndex
Adds the lanes of this vector to their corresponding lane numbers, scaled by a given constant. This is a lanewise unary operation which, for each laneN
, computes the scaled index valueN*scale
and adds it to the value already in laneN
of the current vector.The scale must not be so large, and the element size must not be so small, that that there would be an overflow when computing any of the
N*scale
orVLENGTH*scale
, when the result is represented using the vector lane typeETYPE
.The following pseudocode illustrates this behavior:
Vector<E> a = ...; VectorSpecies<E> species = a.species(); ETYPE[] ar = a.toArray(); for (int i = 0; i < ar.length; i++) { long d = (long)i * scale; if (d != (ETYPE) d) throw ...; ar[i] += (ETYPE) d; } long d = (long)ar.length * scale; if (d != (ETYPE) d) throw ...; return EVector.fromArray(s, ar, 0);
 Parameters:
scale
 the number to multiply by each lane indexN
, typically1
 Returns:
 the result of incrementing each lane element by its
corresponding lane index
N
, scaled byscale
 Throws:
IllegalArgumentException
 if the values in the interval[0..VLENGTH*scale]
are not representable by theETYPE

slice
Slices a segment of adjacent lanes, starting at a givenorigin
lane in the current vector, and continuing (as needed) into an immediately following vector. The block ofVLENGTH
lanes is extracted into its own vector and returned.This is a crosslane operation that shifts lane elements to the front, from the current vector and the second vector. Both vectors can be viewed as a combined "background" of length
2*VLENGTH
, from which a slice is extracted. The lane numberedN
in the output vector is copied from laneorigin+N
of the input vector, if that lane exists, else from laneorigin+NVLENGTH
of the second vector (which is guaranteed to exist).The
origin
value must be in the inclusive range0..VLENGTH
. As limiting cases,v.slice(0,w)
andv.slice(VLENGTH,w)
returnv
andw
, respectively. API Note:
 This method may be regarded as the inverse of
unslice()
, in that the sliced value could be unsliced back into its original position in the two input vectors, without disturbing unrelated elements, as in the following pseudocode:EVector slice = v1.slice(origin, v2); EVector w1 = slice.unslice(origin, v1, 0); EVector w2 = slice.unslice(origin, v2, 1); assert v1.equals(w1); assert v2.equals(w2);
This method also supports a variety of crosslane shifts and rotates as follows:
 To shift lanes forward to the front of the vector, supply a
zero vector for the second operand and specify the shift count
as the origin. For example:
v.slice(shift, v.broadcast(0))
.  To shift lanes backward to the back of the vector, supply a
zero vector for the first operand, and specify the
negative shift count as the origin (modulo
VLENGTH
. For example:v.broadcast(0).slice(v.length()shift, v)
.  To rotate lanes forward toward the front end of the vector,
cycling the earliest lanes around to the back, supply the same
vector for both operands and specify the rotate count as the
origin. For example:
v.slice(rotate, v)
.  To rotate lanes backward toward the back end of the vector,
cycling the latest lanes around to the front, supply the same
vector for both operands and specify the negative of the rotate
count (modulo
VLENGTH
) as the origin. For example:v.slice(v.length()  rotate, v)
. 
Since
origin
values less then zero or more thanVLENGTH
will be rejected, if you need to rotate by an unpredictable multiple ofVLENGTH
, be sure to reduce the origin value into the required range. TheloopBound()
method can help with this. For example:v.slice(rotate  v.species().loopBound(rotate), v)
.
 To shift lanes forward to the front of the vector, supply a
zero vector for the second operand and specify the shift count
as the origin. For example:
 Parameters:
origin
 the first input lane to transfer into the slicev1
 a second vector logically concatenated with the first, before the slice is taken (if omitted it defaults to zero) Returns:
 a contiguous slice of
VLENGTH
lanes, taken from this vector starting at the indicated origin, and continuing (as needed) into the second vector  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
 See Also:

slice
Slices a segment of adjacent lanes under the control of a mask, starting at a givenorigin
lane in the current vector, and continuing (as needed) into an immediately following vector. The block ofVLENGTH
lanes is extracted into its own vector and returned. The resulting vector will be zero in all lanes unset in the given mask. Lanes set in the mask will contain data copied from selected lanes ofthis
orv1
.This is a crosslane operation that shifts lane elements to the front, from the current vector and the second vector. Both vectors can be viewed as a combined "background" of length
2*VLENGTH
, from which a slice is extracted. The returned result is equal to the expressionbroadcast(0).blend(slice(origin,v1),m)
. API Note:
 This method may be regarded as the inverse of
#unslice(int,Vector,int,VectorMask) unslice()
, in that the sliced value could be unsliced back into its original position in the two input vectors, without disturbing unrelated elements, as in the following pseudocode:EVector slice = v1.slice(origin, v2, m); EVector w1 = slice.unslice(origin, v1, 0, m); EVector w2 = slice.unslice(origin, v2, 1, m); assert v1.equals(w1); assert v2.equals(w2);
 Parameters:
origin
 the first input lane to transfer into the slicev1
 a second vector logically concatenated with the first, before the slice is taken (if omitted it defaults to zero)m
 the mask controlling lane selection into the resulting vector Returns:
 a contiguous slice of
VLENGTH
lanes, taken from this vector starting at the indicated origin, and continuing (as needed) into the second vector  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
 See Also:

slice
Slices a segment of adjacent lanes, starting at a givenorigin
lane in the current vector. A block ofVLENGTH
lanes, possibly padded with zero lanes, is extracted into its own vector and returned. This is a convenience method which slices from a single vector against an extended background of zero lanes. It is equivalent toslice
(origin,
broadcast
(0))
. It may also be viewed simply as a crosslane shift from later to earlier lanes, with zeroes filling in the vacated lanes at the end of the vector. In this view, the shift count isorigin
. Parameters:
origin
 the first input lane to transfer into the slice Returns:
 the last
VLENGTHorigin
input lanes, placed starting in the first lane of the output, padded at the end with zeroes  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
 See Also:

unslice
Reverses a slice(), inserting the current vector as a slice within another "background" input vector, which is regarded as one or the other input to a hypothetical subsequentslice()
operation.This is a crosslane operation that permutes the lane elements of the current vector toward the back and inserts them into a logical pair of background vectors. Only one of the pair will be returned, however. The background is formed by duplicating the second input vector. (However, the output will never contain two duplicates from the same input lane.) The lane numbered
N
in the input vector is copied into laneorigin+N
of the first background vector, if that lane exists, else into laneorigin+NVLENGTH
of the second background vector (which is guaranteed to exist). The first or second background vector, updated with the inserted slice, is returned. Thepart
number of zero or one selects the first or second updated background vector.The
origin
value must be in the inclusive range0..VLENGTH
. As limiting cases,v.unslice(0,w,0)
andv.unslice(VLENGTH,w,1)
both returnv
, whilev.unslice(0,w,1)
andv.unslice(VLENGTH,w,0)
both returnw
. API Note:
 This method supports a variety of crosslane insertion
operations as follows:
 To insert near the end of a background vector
w
at some offset, specify the offset as the origin and select part zero. For example:v.unslice(offset, w, 0)
.  To insert near the end of a background vector
w
, but capturing the overflow into the next vectorx
, specify the offset as the origin and select part one. For example:v.unslice(offset, x, 1)
.  To insert the last
N
items near the beginning of a background vectorw
, supply aVLENGTHN
as the origin and select part one. For example:v.unslice(v.length()N, w)
.
 To insert near the end of a background vector
 Parameters:
origin
 the first output lane to receive the slicew
 the background vector that (as two copies) will receive the inserted slicepart
 the part number of the result (either zero or one) Returns:
 either the first or second part of a pair of
background vectors
w
, updated by inserting this vector at the indicated origin  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
, or ifpart
is not zero or one See Also:

unslice
Reverses a slice(), inserting (under the control of a mask) the current vector as a slice within another "background" input vector, which is regarded as one or the other input to a hypothetical subsequentslice()
operation.This is a crosslane operation that permutes the lane elements of the current vector forward and inserts its lanes (when selected by the mask) into a logical pair of background vectors. As with the unmasked version of this method, only one of the pair will be returned, as selected by the
part
number. For each laneN
selected by the mask, the lane value is copied into laneorigin+N
of the first background vector, if that lane exists, else into laneorigin+NVLENGTH
of the second background vector (which is guaranteed to exist). Background lanes retain their original values if the corresponding input lanesN
are unset in the mask. The first or second background vector, updated with set lanes of the inserted slice, is returned. Thepart
number of zero or one selects the first or second updated background vector. Parameters:
origin
 the first output lane to receive the slicew
 the background vector that (as two copies) will receive the inserted slice, if they are set inm
part
 the part number of the result (either zero or one)m
 the mask controlling lane selection from the current vector Returns:
 either the first or second part of a pair of
background vectors
w
, updated by inserting selected lanes of this vector at the indicated origin  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
, or ifpart
is not zero or one See Also:

unslice
Reverses a slice(), inserting the current vector as a slice within a "background" input of zero lane values. Compared to otherunslice()
methods, this method only returns the first of the pair of background vectors. This is a convenience method which returns the result ofunslice
(origin,
broadcast
(0), 0)
. It may also be viewed simply as a crosslane shift from earlier to later lanes, with zeroes filling in the vacated lanes at the beginning of the vector. In this view, the shift count isorigin
. Parameters:
origin
 the first output lane to receive the slice Returns:
 the first
VLENGTHorigin
input lanes, placed starting at the given origin, padded at the beginning with zeroes  Throws:
ArrayIndexOutOfBoundsException
 iforigin
is negative or greater thanVLENGTH
 See Also:

rearrange
Rearranges the lane elements of this vector, selecting lanes under the control of a specific shuffle. This is a crosslane operation that rearranges the lane elements of this vector. For each laneN
of the shuffle, and for each lane source indexI=s.wrapIndex(s.laneSource(N))
in the shuffle, the output laneN
obtains the value from the input vector at laneI
. Parameters:
s
 the shuffle controlling lane index selection Returns:
 the rearrangement of the lane elements of this vector
 See Also:

rearrange
Rearranges the lane elements of this vector, selecting lanes under the control of a specific shuffle and a mask. This is a crosslane operation that rearranges the lane elements of this vector. For each laneN
of the shuffle, and for each lane source indexI=s.wrapIndex(s.laneSource(N))
in the shuffle, the output laneN
obtains the value from the input vector at laneI
if the mask is set. Otherwise the output laneN
is set to zero.This method returns the value of this pseudocode:
Vector<E> r = this.rearrange(s); return broadcast(0).blend(r, m);
 Parameters:
s
 the shuffle controlling lane index selectionm
 the mask controlling application of the shuffle Returns:
 the rearrangement of the lane elements of this vector
 See Also:

rearrange
Rearranges the lane elements of two vectors, selecting lanes under the control of a specific shuffle, using both normal and exceptional indexes in the shuffle to steer data. This is a crosslane operation that rearranges the lane elements of the two input vectors (the current vector and a second vectorv
). For each laneN
of the shuffle, and for each lane source indexI=s.laneSource(N)
in the shuffle, the output laneN
obtains the value from the first vector at laneI
ifI>=0
. Otherwise, the exceptional indexI
is wrapped by addingVLENGTH
to it and used to index the second vector, at indexI+VLENGTH
.This method returns the value of this pseudocode:
Vector<E> r1 = this.rearrange(s.wrapIndexes()); // or else: r1 = this.rearrange(s, s.laneIsValid()); Vector<E> r2 = v.rearrange(s.wrapIndexes()); return r2.blend(r1,s.laneIsValid());
 Parameters:
s
 the shuffle controlling lane selection from both input vectorsv
 the second input vector Returns:
 the rearrangement of lane elements of this vector and a second input vector
 See Also:

compress
Compresses the lane elements of this vector selecting lanes under the control of a specific mask. This is a crosslane operation that compresses the lane elements of this vector as selected by the specified mask. For each laneN
of the mask, if the mask at laneN
is set, the element at laneN
of input vector is selected and stored into the output vector contiguously starting from the lane0
. All the upper remaining lanes, if any, of the output vector are set to zero. Parameters:
m
 the mask controlling the compression Returns:
 the compressed lane elements of this vector
 Since:
 19

expand
Expands the lane elements of this vector under the control of a specific mask. This is a crosslane operation that expands the contiguous lane elements of this vector into lanes of an output vector as selected by the specified mask. For each laneN
of the mask, if the mask at laneN
is set, the next contiguous element of input vector starting from lane0
is selected and stored into the output vector at laneN
. All the remaining lanes, if any, of the output vector are set to zero. Parameters:
m
 the mask controlling the compression Returns:
 the expanded lane elements of this vector
 Since:
 19

selectFrom
Using index values stored in the lanes of this vector, assemble values stored in second vectorv
. The second vector thus serves as a table, whose elements are selected by indexes in the current vector. This is a crosslane operation that rearranges the lane elements of the argument vector, under the control of this vector. For each laneN
of this vector, and for each lane valueI=wrapIndex(this.lane(N))
in this vector, the output laneN
obtains the value from the argument vector at laneI
. In this way, the result contains only values stored in the argument vectorv
, but presented in an order which depends on the index values inthis
. The result is the same as the expressionv.rearrange(this.toShuffle())
. Parameters:
v
 the vector supplying the result values Returns:
 the rearrangement of the lane elements of
v
 See Also:

selectFrom
Using values stored in the lanes of this vector, assemble values stored in the second vectorv1
and third vectorv2
. The second and third vectors thus serve as a table, whose elements are selected by indexes in this vector. This is a crosslane operation that rearranges the lane elements of the argument vectors, under the control of this vector. For each laneN
of this vector, and for each lane valueI=wrapIndex(this.lane(N)
in this vector, the output laneN
obtains the value from the second vector at laneI
ifI < VLENGTH
. Otherwise, the output laneN
obtains the value from the third vector at laneI  VLENGTH
. Here,VLENGTH
is the result ofthis.length()
, and for integral valueswrapIndex
computes the result ofMath.floorMod(E, 2 * VLENGTH)
, whereE
is the index to be wrapped. As long asVLENGTH
is a power of two, then the result is also equal toE & (2 * VLENGTH  1)
. For floating point valueswrapIndex
computesMath.floorMod(convert(E), 2 * VLENGTH)
, whereconvert
converts the floating point value to an integral value with the same number of representational bits  as in converting a double value to a long value ((long)doubleVal
), or a float value to an int value ((int)floatVal
). In this way, the result contains only values stored in the argument vectorsv1
andv2
, but presented in an order which depends on the index values inthis
. The result for integral values is the same as the expressionv1.rearrange( this.lanewise(VectorOperators.AND, 2 * VLENGTH  1).toShuffle(), v2)
VLENGTH
is a power of two. The lanewiseAND
operation results in a vector whose elements are in the range[0, 2 * VLENGTH  1])
. The shuffle conversion results in a partially wrapped shuffle whose indexes are in the range[VLENGTH, VLENGTH  1])
, where exceptional indexes are used to select elements in the third vector. Parameters:
v1
 the first input vectorv2
 the second input vector Returns:
 the rearrangement of lane elements of
v1
andv2
 See Also:

selectFrom
Using index values stored in the lanes of this vector, assemble values stored in second vector, under the control of a mask. Using index values stored in the lanes of this vector, assemble values stored in second vectorv
. The second vector thus serves as a table, whose elements are selected by indexes in the current vector. Lanes that are unset in the mask receive a zero rather than a value from the table. This is a crosslane operation that rearranges the lane elements of the argument vector, under the control of this vector and the mask. The result is the same as the expressionv.rearrange(this.toShuffle(), m)
. Parameters:
v
 the vector supplying the result valuesm
 the mask controlling selection fromv
 Returns:
 the rearrangement of the lane elements of
v
 See Also:

broadcast
Returns a vector of the same species as this one where all lane elements are set to the primitive valuee
. The contents of the current vector are discarded; only the species is relevant to this operation.This method returns the value of this expression:
EVector.broadcast(this.species(), (ETYPE)e)
, whereEVector
is the vector class specific to this vector's element typeETYPE
.The
long
valuee
must be accurately representable by theETYPE
of this vector's species, so thate==(long)(ETYPE)e
. If this rule is violated the problem is not detected statically, but anIllegalArgumentException
is thrown at runtime. Thus, this method somewhat weakens the static type checking of immediate constants and other scalars, but it makes up for this by improving the expressiveness of the generic API. Note that ane
value in the range[128..127]
is always acceptable, since everyETYPE
will accept everybyte
value. API Note:
 Subtypes improve on this method by sharpening
the method return type and
and the type of the scalar parameter
e
.  Parameters:
e
 the value to broadcast Returns:
 a vector where all lane elements are set to
the primitive value
e
 Throws:
IllegalArgumentException
 if the givenlong
value cannot be represented by the vector'sETYPE
 See Also:

maskAll
Returns a mask of same species as this vector, where each lane is set or unset according to given single boolean, which is broadcast to all lanes.This method returns the value of this expression:
species().maskAll(bit)
. Parameters:
bit
 the given mask bit to be replicated Returns:
 a mask where each lane is set or unset according to the given bit
 See Also:

toShuffle
Converts this vector into a shuffle, converting the lane values toint
and regarding them as source indexes.This method behaves as if it returns the result of creating a shuffle given an array of the vector elements, as follows:
long[] a = this.toLongArray(); int[] sa = new int[a.length]; for (int i = 0; i < a.length; i++) { sa[i] = (int) a[i]; } return VectorShuffle.fromValues(this.species(), sa);
 Returns:
 a shuffle representation of this vector
 See Also:

reinterpretShape
Transforms this vector to a vector of the given species of element typeF
, reinterpreting the bytes of this vector without performing any value conversions.Depending on the selected species, this operation may either expand or contract its logical result, in which case a nonzero
part
number can further control the selection and steering of the logical result into the physical output vector.The underlying bits of this vector are copied to the resulting vector without modification, but those bits, before copying, may be truncated if the this vector's bitsize is greater than desired vector's bit size, or filled with zero bits if this vector's bitsize is less than desired vector's bitsize.
If the old and new species have different shape, this is a shapechanging operation, and may have special implementation costs.
The method behaves as if this vector is stored into a byte array using littleendian byte ordering and then the desired vector is loaded from the same byte array using the same ordering.
The following pseudocode illustrates the behavior:
int domSize = this.byteSize(); int ranSize = species.vectorByteSize(); int M = (domSize > ranSize ? domSize / ranSize : ranSize / domSize); assert Math.abs(part) < M; assert (part == 0)  (part > 0) == (domSize > ranSize); MemorySegment ms = MemorySegment.ofArray(new byte[Math.max(domSize, ranSize)]); if (domSize > ranSize) { // expansion this.intoMemorySegment(ms, 0, ByteOrder.native()); int origin = part * ranSize; return species.fromMemorySegment(ms, origin, ByteOrder.native()); } else { // contraction or sizeinvariant int origin = (part) * domSize; this.intoMemorySegment(ms, origin, ByteOrder.native()); return species.fromMemorySegment(ms, 0, ByteOrder.native()); }
 API Note:
 Although this method is defined as if the vectors in question were loaded or stored into memory, memory semantics has little to do or nothing with the actual implementation. The appeal to littleendian ordering is simply a shorthand for what could otherwise be a large number of detailed rules concerning the mapping between lanestructured vectors and bytestructured vectors.
 Type Parameters:
F
 the boxed element type of the species Parameters:
species
 the desired vector speciespart
 the part number of the result, or zero if neither expanding nor contracting Returns:
 a vector transformed, by shape and element type, from this vector
 See Also:

reinterpretAsBytes
Views this vector as a vector of the same shape and contents but a lane type ofbyte
, where the bytes are extracted from the lanes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(byte.class))
. It may be considered an inverse to the various methods which consolidate bytes into larger lanes within the same vector, such asreinterpretAsInts()
. Returns:
 a
ByteVector
with the same shape and information content  See Also:

reinterpretAsShorts
Reinterprets this vector as a vector of the same shape and contents but a lane type ofshort
, where the lanes are assembled from successive bytes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(short.class))
. It may be considered an inverse toreinterpretAsBytes()
. Returns:
 a
ShortVector
with the same shape and information content

reinterpretAsInts
Reinterprets this vector as a vector of the same shape and contents but a lane type ofint
, where the lanes are assembled from successive bytes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(int.class))
. It may be considered an inverse toreinterpretAsBytes()
. Returns:
 a
IntVector
with the same shape and information content

reinterpretAsLongs
Reinterprets this vector as a vector of the same shape and contents but a lane type oflong
, where the lanes are assembled from successive bytes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(long.class))
. It may be considered an inverse toreinterpretAsBytes()
. Returns:
 a
LongVector
with the same shape and information content

reinterpretAsFloats
Reinterprets this vector as a vector of the same shape and contents but a lane type offloat
, where the lanes are assembled from successive bytes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(float.class))
. It may be considered an inverse toreinterpretAsBytes()
. Returns:
 a
FloatVector
with the same shape and information content

reinterpretAsDoubles
Reinterprets this vector as a vector of the same shape and contents but a lane type ofdouble
, where the lanes are assembled from successive bytes according to littleendian order. It is a convenience method for the expressionreinterpretShape(species().withLanes(double.class))
. It may be considered an inverse toreinterpretAsBytes()
. Returns:
 a
DoubleVector
with the same shape and information content

viewAsIntegralLanes
Views this vector as a vector of the same shape, length, and contents, but a lane type that is not a floatingpoint type. This is a lanewise reinterpretation cast on the lane values. As such, this method does not changeVSHAPE
orVLENGTH
, and there is no change to the bitwise contents of the vector. If the vector'sETYPE
is already an integral type, the same vector is returned unchanged. This method returns the value of this expression:convert(conv,0)
, whereconv
isVectorOperators.Conversion.ofReinterpret(E.class,F.class)
, andF
is the nonfloatingpoint type of the same size asE
. API Note:
 Subtypes improve on this method by sharpening the return type.
 Returns:
 the original vector, reinterpreted as nonfloating point
 See Also:

viewAsFloatingLanes
Views this vector as a vector of the same shape, length, and contents, but a lane type that is a floatingpoint type. This is a lanewise reinterpretation cast on the lane values. As such, there this method does not changeVSHAPE
orVLENGTH
, and there is no change to the bitwise contents of the vector. If the vector'sETYPE
is already a floatpoint type, the same vector is returned unchanged. If the vector's element size does not match any floating point type size, anIllegalArgumentException
is thrown. This method returns the value of this expression:convert(conv,0)
, whereconv
isVectorOperators.Conversion.ofReinterpret(E.class,F.class)
, andF
is the floatingpoint type of the same size asE
, if any. API Note:
 Subtypes improve on this method by sharpening the return type.
 Returns:
 the original vector, reinterpreted as floating point
 Throws:
UnsupportedOperationException
 if there is no floating point type the same size as the lanes of this vector See Also:

convert
Convert this vector to a vector of the same shape and a new element type, converting lane values from the currentETYPE
to a new lane type (calledFTYPE
here) according to the indicated conversion. This is a lanewise shapeinvariant operation which copiesETYPE
values from the input vector to correspondingFTYPE
values in the result. Depending on the selected conversion, this operation may either expand or contract its logical result, in which case a nonzeropart
number can further control the selection and steering of the logical result into the physical output vector.Each specific conversion is described by a conversion constant in the class
VectorOperators
. Each conversion operator has a specified domain type and range type. The domain type must exactly match the lane type of the input vector, while the range type determines the lane type of the output vectors.A conversion operator may be classified as (respectively) inplace, expanding, or contracting, depending on whether the bitsize of its domain type is (respectively) equal, less than, or greater than the bitsize of its range type.
Independently, conversion operations can also be classified as reinterpreting or valuetransforming, depending on whether the conversion copies representation bits unchanged, or changes the representation bits in order to retain (part or all of) the logical value of the input value.
If a reinterpreting conversion contracts, it will truncate the upper bits of the input. If it expands, it will pad upper bits of the output with zero bits, when there are no corresponding input bits.
An expanding conversion such as
S2I
(short
value toint
) takes a scalar value and represents it in a larger format (always with some information redundancy). A contracting conversion such asD2F
(double
value tofloat
) takes a scalar value and represents it in a smaller format (always with some information loss). Some inplace conversions may also include information loss, such asL2D
(long
value todouble
) orF2I
(float
value toint
). Reinterpreting inplace conversions are not lossy, unless the bitwise value is somehow not legal in the output type. Converting the bitpattern of aNaN
may discard bits from theNaN
's significand.This classification is important, because, unless otherwise documented, conversion operations never change vector shape, regardless of how they may change lane sizes. Therefore an expanding conversion cannot store all of its results in its output vector, because the output vector has fewer lanes of larger size, in order to have the same overall bitsize as its input. Likewise, a contracting conversion must store its relatively small results into a subset of the lanes of the output vector, defaulting the unused lanes to zero.
As an example, a conversion from
byte
tolong
(M=8
) will discard 87.5% of the input values in order to convert the remaining 12.5% into the roomylong
lanes of the output vector. The inverse conversion will convert back all of the large results, but will waste 87.5% of the lanes in the output vector. Inplace conversions (M=1
) deliver all of their results in one output vector, without wasting lanes.To manage the details of these expansions and contractions, a nonzero
part
parameter selects partial results from expansions, or steers the results of contractions into corresponding locations, as follows: expanding by
M
:part
must be in the range[0..M1]
, and selects the block ofVLENGTH/M
input lanes starting at the origin lane atpart*VLENGTH/M
.The
VLENGTH/M
output lanes represent a partial slice of the whole logical result of the conversion, filling the entire physical output vector.  contracting by
M
:part
must be in the range[M+1..0]
, and steers allVLENGTH
input lanes into the output located at the origin lanepart*VLENGTH
. There is a total ofVLENGTH*M
output lanes, and those not holding converted input values are filled with zeroes.A group of such output vectors, with logical result parts steered to disjoint blocks, can be reassembled using the bitwise or or (for floating point) the
FIRST_NONZERO
operator.  inplace (
M=1
):part
must be zero. Both vectors have the sameVLENGTH
. The result is always positioned at the origin lane of zero.
This method is a restricted version of the more general but less frequently used shapechanging method
convertShape()
. The result of this method is the same as the expressionthis.convertShape(conv, rsp, this.broadcast(part))
, where the output species isrsp=this.species().withLanes(FTYPE.class)
. Type Parameters:
F
 the boxed element type of the species Parameters:
conv
 the desired scalar conversion to apply lanewisepart
 the part number of the result, or zero if neither expanding nor contracting Returns:
 a vector converted by shape and element type from this vector
 Throws:
ArrayIndexOutOfBoundsException
 unlesspart
is zero, or else the expansion ratio isM
andpart
is positive and less thanM
, or else the contraction ratio isM
andpart
is negative and greaterM
 See Also:
 expanding by

convertShape
public abstract <F> Vector<F> convertShape(VectorOperators.Conversion<E, F> conv, VectorSpecies<F> rsp, int part) Converts this vector to a vector of the given species, shape and element type, converting lane values from the currentETYPE
to a new lane type (calledFTYPE
here) according to the indicated conversion. This is a lanewise operation which copiesETYPE
values from the input vector to correspondingFTYPE
values in the result.If the old and new species have the same shape, the behavior is exactly the same as the simpler, shapeinvariant method
convert()
. In such cases, the simpler methodconvert()
should be used, to make code easier to reason about. Otherwise, this is a shapechanging operation, and may have special implementation costs.As a combined effect of shape changes and lane size changes, the input and output species may have different lane counts, causing expansion or contraction. In this case a nonzero
part
parameter selects partial results from an expanded logical result, or steers the results of a contracted logical result into a physical output vector of the required output species.The following pseudocode illustrates the behavior of this method for inplace, expanding, and contracting conversions. (This pseudocode also applies to the shapeinvariant method, but with shape restrictions on the output species.) Note that only one of the three code paths is relevant to any particular combination of conversion operator and shapes.
FTYPE scalar_conversion_op(ETYPE s); EVector a = ...; VectorSpecies<F> rsp = ...; int part = ...; VectorSpecies<E> dsp = a.species(); int domlen = dsp.length(); int ranlen = rsp.length(); FTYPE[] logical = new FTYPE[domlen]; for (int i = 0; i < domlen; i++) { logical[i] = scalar_conversion_op(a.lane(i)); } FTYPE[] physical; if (domlen == ranlen) { // inplace assert part == 0; //else AIOOBE physical = logical; } else if (domlen > ranlen) { // expanding int M = domlen / ranlen; assert 0 <= part && part < M; //else AIOOBE int origin = part * ranlen; physical = Arrays.copyOfRange(logical, origin, origin + ranlen); } else { // (domlen < ranlen) // contracting int M = ranlen / domlen; assert 0 >= part && part > M; //else AIOOBE int origin = part * domlen; System.arraycopy(logical, 0, physical, origin, domlen); } return FVector.fromArray(ran, physical, 0);
 Type Parameters:
F
 the boxed element type of the output species Parameters:
conv
 the desired scalar conversion to apply lanewisersp
 the desired output speciespart
 the part number of the result, or zero if neither expanding nor contracting Returns:
 a vector converted by element type from this vector
 See Also:

castShape
Convenience method for converting a vector from one lane type to another, reshaping as needed when lane sizes change. This method returns the value of this expression:convertShape(conv,rsp,part)
, whereconv
isVectorOperators.Conversion.ofCast(E.class,F.class)
.If the old and new species have different shape, this is a shapechanging operation, and may have special implementation costs.
 Type Parameters:
F
 the boxed element type of the output species Parameters:
rsp
 the desired output speciespart
 the part number of the result, or zero if neither expanding nor contracting Returns:
 a vector converted by element type from this vector
 See Also:

check
Checks that this vector has the given element type, and returns this vector unchanged. The effect is similar to this pseudocode:elementType == species().elementType() ? this : throw new ClassCastException()
. Type Parameters:
F
 the boxed element type of the required lane type Parameters:
elementType
 the required lane type Returns:
 the same vector
 Throws:
ClassCastException
 if the vector has the wrong element type See Also:

check
Checks that this vector has the given species, and returns this vector unchanged. The effect is similar to this pseudocode:species == species() ? this : throw new ClassCastException()
. Type Parameters:
F
 the boxed element type of the required species Parameters:
species
 the required species Returns:
 the same vector
 Throws:
ClassCastException
 if the vector has the wrong species See Also:

intoMemorySegment
Stores this vector into a memory segment starting at an offset using explicit byte order.Bytes are extracted from primitive lane elements according to the specified byte ordering. The lanes are stored according to their memory ordering.
This method behaves as if it calls
intoMemorySegment()
as follows:var m = maskAll(true); intoMemorySegment(ms, offset, bo, m);
 Parameters:
ms
 the memory segmentoffset
 the offset into the memory segmentbo
 the intended byte order Throws:
IndexOutOfBoundsException
 ifoffset+N*ESIZE < 0
oroffset+(N+1)*ESIZE > ms.byteSize()
for any laneN
in the vectorUnsupportedOperationException
 if the memory segment is readonlyIllegalArgumentException
 if the memory segment is a heap segment that is not backed by abyte[]
array.IllegalStateException
 if the memory segment's session is not alive, or if access occurs from a thread other than the thread owning the session. Since:
 19

intoMemorySegment
public abstract void intoMemorySegment(MemorySegment ms, long offset, ByteOrder bo, VectorMask<E> m) Stores this vector into a memory segment starting at an offset using explicit byte order and a mask.Bytes are extracted from primitive lane elements according to the specified byte ordering. The lanes are stored according to their memory ordering.
The following pseudocode illustrates the behavior, where
JAVA_E
is the layout of the primitive element type,ETYPE
is the primitive element type, andEVector
is the primitive vector type for this vector:ETYPE[] a = this.toArray(); var slice = ms.asSlice(offset) for (int n = 0; n < a.length; n++) { if (m.laneIsSet(n)) { slice.setAtIndex(ValueLayout.JAVA_E.withBitAlignment(8), n); } }
 Implementation Note:
 This operation is likely to be more efficient if
the specified byte order is the same as
the platform native order,
since this method will not need to reorder
the bytes of lane values.
In the special case where
ETYPE
isbyte
, the byte order argument is ignored.  Parameters:
ms
 the memory segmentoffset
 the offset into the memory segmentbo
 the intended byte orderm
 the mask controlling lane selection Throws:
IndexOutOfBoundsException
 ifoffset+N*ESIZE < 0
oroffset+(N+1)*ESIZE > ms.byteSize()
for any laneN
in the vector where the mask is setUnsupportedOperationException
 if the memory segment is readonlyIllegalArgumentException
 if the memory segment is a heap segment that is not backed by abyte[]
array.IllegalStateException
 if the memory segment's session is not alive, or if access occurs from a thread other than the thread owning the session. Since:
 19

toArray
Returns a packed array containing all the lane values. The array length is the same as the vector length. The element type of the array is the same as the element type of the vector. The array elements are stored in lane order. Overrides of this method on subtypes ofVector
which specify the element type have an accurately typed array result. API Note:
 Usually strongly typed access is preferable, if you are working with a vector subtype that has a known element type.
 Returns:
 an accurately typed array containing the lane values of this vector
 See Also:

toIntArray
public abstract int[] toIntArray()Returns anint[]
array containing all the lane values, converted to the typeint
. The array length is the same as the vector length. The array elements are converted as if by casting and stored in lane order. This operation may fail if the vector element type isfloat
ordouble
, when lanes contain fractional or outofrange values. If any vector lane value is not representable as anint
, an exception is thrown. API Note:
 Usually strongly typed access is preferable, if you are working with a vector subtype that has a known element type.
 Returns:
 an
int[]
array containing the lane values of this vector  Throws:
UnsupportedOperationException
 if any lane value cannot be represented as anint
array element See Also:

toLongArray
public abstract long[] toLongArray()Returns along[]
array containing all the lane values, converted to the typelong
. The array length is the same as the vector length. The array elements are converted as if by casting and stored in lane order. This operation may fail if the vector element type isfloat
ordouble
, when lanes contain fractional or outofrange values. If any vector lane value is not representable as along
, an exception is thrown. API Note:
 Usually strongly typed access is preferable, if you are working with a vector subtype that has a known element type.
 Returns:
 a
long[]
array containing the lane values of this vector  Throws:
UnsupportedOperationException
 if any lane value cannot be represented as along
array element See Also:

toDoubleArray
public abstract double[] toDoubleArray()Returns adouble[]
array containing all the lane values, converted to the typedouble
. The array length is the same as the vector length. The array elements are converted as if by casting and stored in lane order. This operation can lose precision if the vector element type islong
. API Note:
 Usually
strongly typed access
is preferable, if you are working with a vector subtype that has a known element type.  Returns:
 a
double[]
array containing the lane values of this vector, possibly rounded to representabledouble
values  See Also:

toString
Returns a string representation of this vector, of the form"[0,1,2...]"
, reporting the lane values of this vector, in lane order. The string is produced as if by a call toArrays.toString()
, as appropriate to the array returned bythis.toArray()
. 
equals
Indicates whether this vector is identical to some other object. Two vectors are identical only if they have the same species and same lane values, in the same order.The comparison of lane values is produced as if by a call to
Arrays.equals()
, as appropriate to the arrays returned bytoArray()
on both vectors. 
hashCode

getPayload
