| __kernel kernel __attribute__((vec_type_hint(<typen>))) __attribute__((work_group_size_hint(X, Y, Z))) __attribute__((reqd_work_group_size(X, Y, Z))) |
Description
The __kernel (or kernel) qualifier
declares a function to be a kernel that can be
executed by an application on an OpenCL device(s).
The following rules apply to functions that
are declared with this qualifier:
-
It can be executed on the device only
-
It can be called by the host
-
It is just a regular function call if a
__kernelfunction is called by another kernel function.
The __kernel qualifier can be used with the
keyword __attribute__
to declare additional information about the kernel function as
described below.
The optional __attribute__((vec_type_hint(<typen>)))
is a hint to the compiler and is intended to be a representation of
the computational width of the __kernel,
and should serve as the basis for calculating processor bandwidth
utilization when the compiler is looking to autovectorize the code.
vec_type_hint (<typen>)
shall be one of the built-in scalar or vector data type described in
tables 6.1 and 6.2.
If
vec_type_hint (<typen>)
is not specified, the default value is int.
The
__attribute__((vec_type_hint(int)))
is the default type.
For example, where the developer specified a width of float4,
the compiler should assume
that the computation usually uses up 4 lanes of a float vector,
and would decide to merge work-items or possibly even separate
one work-item into many threads to better match the hardware
capabilities. A conforming implementation is not required
to autovectorize code, but shall
support the hint. A compiler may autovectorize, even if no
hint is provided. If an
implementation merges N work-items into one thread,
it is responsible for correctly handling
cases where the number of global or local work-items
in any dimension modulo N is not zero.
If for example, a __kernel is declared with
__attribute__(( vec_type_hint (float4))) (meaning that most operations in the __kernel are explicitly vectorized using
float4) and the kernel is running using Intel® Advanced Vector Instructions (Intel® AVX) which implements a 8-float-wide vector unit,
the autovectorizer might choose to merge two work-items to one thread, running a second work-item in the high half of the 256-bit AVX register.
As another example, a Power4 machine has two scalar double precision floating-point units with an 6-cycle deep pipe. An autovectorizer for the Power4 machine might choose to interleave six __attribute__(( vec_type_hint (double2))) __kernels into one hardware thread, to ensure that there is always 12-way parallelism available to saturate the FPUs. It might also choose to merge 4 or 8 work-items (or some other number) if it concludes that these are better choices, due to resource utilization concerns or some preference for divisibility by 2.
The optional
__attribute__((work_group_size_hint(X, Y, Z)))
is a hint to the compiler and is intended to specify the work-group size
that may be used i.e. value most likely to be specified by the local_work_size argument to
clEnqueueNDRangeKernel.
For example the __attribute__((work_group_size_hint(1, 1, 1)))
is a hint to the compiler that the kernel will most likely be executed
with a work-group size of 1.
The optional
__attribute__((reqd_work_group_size(X, Y, Z)))
is the work-group size that must be used as the local_work_size argument to clEnqueueNDRangeKernel.
This allows the compiler to optimize the generated code appropriately for this kernel. The optional __attribute__((reqd_work_group_size(X, Y, Z))),
if specified, must be (1, 1, 1) if the kernel is executed via
clEnqueueTask.
If Z is one, the work_dim argument to
clEnqueueNDRangeKernel
can be 2 or 3. If Y and Z are
one, the work_dim argument to
clEnqueueNDRangeKernel
can be 1, 2 or 3.
Notes
Implicit in autovectorization is the assumption that any libraries called
from the __kernel must be recompilable at run time to handle cases where
the compiler decides to merge or separate workitems. This probably means
that such libraries can never be hard coded binaries or that hard coded
binaries must be accompanied either by source or some retargetable intermediate
representation. This may be a code security question for some.
Kernel functions with variables declared inside the function with the __local
or local qualifier can be called by the host using appropriate
APIs such as clEnqueueNDRangeKernel,
and clEnqueueTask.
The behavior of calling kernel functions with variables declared inside the function
with the __local and local qualifier from other
kernel functions is implementation-defined.
The __kernel and kernel names are reserved
for use as functions qualifiers and shall not be used otherwise.
Example
| // autovectorize assuming float4 as the // basic computation width __kernel __attribute__((vec_type_hint(float4))) void foo( __global float4 *p ) { .... // autovectorize assuming double as the // basic computation width __kernel __attribute__((vec_type_hint(double))) void foo( __global float4 *p ){ .... // autovectorize assuming int (default) // as the basic computation width __kernel void foo( __global float4 *p ){ .... |
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