Function Qualifiers

Qualifiers for kernel functions.

__kernel kernel __attribute__((vec_type_hint(<typen>))) __attribute__((work_group_size_hint(X, Y, Z))) __attribute__((reqd_work_group_size(X, Y, Z)))


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 __kernel function 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.


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.


// 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 ){ ....


OpenCL Specification

Also see

clEnqueueNDRangeKernel clEnqueueTask

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