OpenCL Attributes

Description

The ALWAYS_INLINE attribute indicates that a function must be inlined. This attribute is a standard feature of GCC, and a standard feature of the SDx tool compilers.

This attribute enables a compiler optimization to have a function inlined into the calling function. The inlined function is dissolved and no longer appears as a separate level of hierarchy in the RTL.

In some cases, inlining a function allows operations within the function to be shared and optimized more effectively with surrounding operations in the calling function. However, an inlined function can no longer be shared with other functions, so the logic might be duplicated between the inlined function and a separate instance of the function which can be more broadly shared. While this can improve performance, this will also increase the area required for implementing the RTL.

For OpenCL kernels, the SDx compiler uses its own rules to inline or not inline a function. To directly control inlining functions, use the ALWAYS_INLINE or NOINLINE attributes.

By default, inlining is only performed on the next level of function hierarchy, not sub-functions.

Syntax

__attribute__((always_inline))

Examples

This example adds the ALWAYS_INLINE attribute to function foo:

__attribute__((always_inline))
  void foo ( a, b, c, d ) {
  ...
}

This example prevents the inlining of the function foo:

__attribute__((noinline))
  void foo ( a, b, c, d ) {
  ...
}

See Also

• https://gcc.gnu.org
• SDAccel Environment Profiling and Optimization Guide

Description

Loop unrolling is the first optimization technique available in the SDAccel development environment. The purpose of the loop unroll optimization is to expose concurrency to the compiler. This newly exposed concurrency reduces latency and improves performance, but also consumes more FPGA fabric resources.

The OPENCL_UNROLL_HINT attribute is part of the OpenCL Language Specification, and specifies that loops (for, while, do) can be unrolled by the OpenCL compiler. See Unrolling Loops for more information.

The OPENCL_UNROLL_HINT attribute qualifier must appear immediately before the loop to be affected. You can use this attribute to specify full unrolling of the loop, partial unrolling by a specified amount, or to disable unrolling of the loop.

Syntax

Place the attribute in the OpenCL source before the loop definition:

__attribute__((opencl_unroll_hint(<n>)))

Where:

• <n> is an optional loop unrolling factor and must be a positive integer, or compile time constant expression. An unroll factor of 1 disables unrolling.
  TIP: If <n> is not specified, the compiler automatically determines the unrolling factor for the loop.

Example 1

The following example unrolls the for loop by a factor of 2. This results in two parallel loop iterations instead of four sequential iterations for the compute unit to complete the operation.

__attribute__((opencl_unroll_hint(2)))
for(int i = 0; i < LENGTH; i++) {
bufc[i] = bufa[i] * bufb[i];
}

Conceptually the compiler transforms the loop above to the following code:

for(int i = 0; i < LENGTH; i+=2) {
bufc[i] = bufa[i] * bufb[i];
bufc[i+1] = bufa[i+1] * bufb[i+1];
}

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

When OpenCL API kernels are submitted for execution on an OpenCL device, they execute within an index space, called an ND range, which can have 1, 2, or 3 dimensions. This is called the global size in the OpenCL API. The work-group size defines the amount of the ND range that can be processed by a single invocation of a kernel compute unit. The work-group size is also called the local size in the OpenCL API. The OpenCL compiler can determine the work-group size based on the properties of the kernel and selected device. After the work-group size (local size) is determined, the ND range (global size) is divided automatically into work-groups, and the work-groups are scheduled for execution on the device.

Although the OpenCL compiler can define the work-group size, the specification of the REQD_WORK_GROUP_SIZE attribute on the kernel to define the work-group size is highly recommended for FPGA implementations of the kernel. The attribute is recommended for performance optimization during the generation of the custom logic for a kernel. See OpenCL Execution Model in the SDAccel Environment Profiling and Optimization Guide (UG1207) for more information.

OpenCL kernel functions are executed exactly one time for each point in the ND range index space. This unit of work for each point in the ND range is called a work-item. Work-items are organized into work-groups, which are the unit of work scheduled onto compute units. The optional REQD_WORK_GROUP_SIZE attribute defines the work-group size of a compute unit 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.

Syntax

Place this attribute before the kernel definition, or before the primary function specified for the kernel:

__attribute__((reqd_work_group_size(<X>, <Y>, <Z>)))

Where:

• <X>, <Y>, <Z>: Specifies the ND range of the kernel. This represents each dimension of a three dimensional matrix specifying the size of the work-group for the kernel.

Examples

The following OpenCL C kernel code shows a vector addition design where two arrays of data are summed into a third array. The required size of the work-group is 16x1x1. This kernel will execute 16 times to produce a valid result.

#include <clc.h>
// For VHLS OpenCL C kernels, the full work group is synthesized
__attribute__ ((reqd_work_group_size(16, 1, 1)))
__kernel void 
vadd(__global int* a,
__global int* b,
__global int* c)
{
int idx = get_global_id(0);
c[idx] = a[idx] + b[idx];
}

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

The optional __attribute__((vec_type_hint(<type>))) is part of the OpenCL Language Specification, and hints to the OpenCL compiler representing the computational width of the kernel, providing a basis for calculating processor bandwidth usage when the compiler is looking to auto-vectorize the code.

By default, the kernel is assumed to have the __attribute__((vec_type_hint(int))) qualifier. This lets you specify a different vectorization type.

Implicit in autovectorization is the assumption that any libraries called from the kernel must be re-compilable at runtime to handle cases where the compiler decides to merge or separate workitems. This means that these libraries can never be hard-coded binaries or that hard-coded binaries must be accompanied either by source or some re-targetable intermediate representation. This might be a code security question for some.

Syntax

__attribute__((vec_type_hint(<type>)))

Where:

• <type>: is one of the built-in vector types listed in the following table, or the constituent scalar element types.
  Note: When not specified, the kernel is assumed to have an INT type.

Examples

The following example autovectorizes assuming double-wide integer as the basic computation width:

#include <clc.h>
// For VHLS OpenCL C kernels, the full work group is synthesized
__attribute__((vec_type_hint(double)))
__attribute__ ((reqd_work_group_size(16, 1, 1)))
__kernel void 
...

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

The work-group size in the OpenCL API standard defines the size of the ND range space that can be handled by a single invocation of a kernel compute unit. When OpenCL kernels are submitted for execution on an OpenCL device, they execute within an index space, called an ND range, which can have 1, 2, or 3 dimensions. See "OpenCL Execution Model" in SDAccel Environment Profiling and Optimization Guide for more information.

OpenCL kernel functions are executed exactly one time for each point in the ND range index space. This unit of work for each point in the ND range is called a work-item. Unlike for loops in C, where loop iterations are executed sequentially and in-order, an OpenCL runtime and device is free to execute work-items in parallel and in any order.

Work-items are organized into work-groups, which are the unit of work scheduled onto compute units. The optional WORK_GROUP_SIZE_HINT attribute is part of the OpenCL Language Specification, and is a hint to the compiler that indicates the work-group size value most likely to be specified by the local_work_size argument to clEnqueueNDRangeKernel. This allows the compiler to optimize the generated code according to the expected value.

Syntax

Place this attribute before the kernel definition, or before the primary function specified for the kernel:

__attribute__((work_group_size_hint(<X>, <Y>, <Z>)))

Where:

• <X>, <Y>, <Z>: Specifies the ND range of the kernel. This represents each dimension of a three dimensional matrix specifying the size of the work-group for the kernel.

Examples

The following example is a hint to the compiler that the kernel will most likely be executed with a work-group size of 1:

__attribute__((work_group_size_hint(1, 1, 1)))
__kernel void
...

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

An advantage of using the FPGA over other compute devices for OpenCL programs is the ability for the application programmer to customize the memory architecture all throughout the system and into the compute unit. By default, the SDAccel compiler generates a memory architecture within the compute unit that maximizes local and private memory bandwidth based on static code analysis of the kernel code. Further optimization of these memories is possible based on attributes in the kernel source code, which can be used to specify physical layouts and implementations of local and private memories. The attribute in the SDAccel compiler to control the physical layout of memories in a compute unit is array_partition.

For one-dimensional arrays, the XCL_ARRAY_PARTITION attribute implements an array declared within kernel code as multiple physical memories instead of a single physical memory. The selection of which partitioning scheme to use depends on the specific application and its performance goals. The array partitioning schemes available in the SDAccel tool compiler are cyclic, block, and complete.

Syntax

Place the attribute with the definition of the array variable:

__attribute__((xcl_array_partition(<type>, <factor>, 
<dimension>)))

Where:

• <type>: Specifies one of the following partition types:
  • cyclic: Cyclic partitioning is the implementation of an array as a set of smaller physical memories that can be accessed simultaneously by the logic in the compute unit. The array is partitioned cyclically by putting one element into each memory before coming back to the first memory to repeat the cycle until the array is fully partitioned.
  • block: Block partitioning is the physical implementation of an array as a set of smaller memories that can be accessed simultaneously by the logic inside of the compute unit. In this case, each memory block is filled with elements from the array before moving on to the next memory.
  • complete: Complete partitioning decomposes the array into individual elements. For a one-dimensional array, this corresponds to resolving a memory into individual registers.
  • The default <type> is complete.
• <factor>: For cyclic type partitioning, the <factor> specifies how many physical memories to partition the original array into in the kernel code. For Block type partitioning, the <factor> specifies the number of elements from the original array to store in each physical memory.
  IMPORTANT: For complete type partitioning, the <factor> is not specified.
• <dimension>: Specifies which array dimension to partition. Specified as an integer from 1 to <N>. SDAccel environment supports arrays of N dimensions and can partition the array on any single dimension.

Example 1

For example, consider the following array declaration:

int buffer[16];

The integer array, named buffer, stores 16 values that are 32-bits wide each. Cyclic partitioning can be applied to this array with the following declaration:

int buffer[16] __attribute__((xcl_array_partition(cyclic,4,1)));

In this example, the cyclic <partition_type> attribute tells the SDAccel compiler to distribute the contents of the array among four physical memories. This attribute increases the immediate memory bandwidth for operations accessing the array buffer by a factor of four.

All arrays inside of a compute unit in the context of the SDAccel environment are capable of sustaining a maximum of two concurrent accesses. By dividing the original array in the code into four physical memories, the resulting compute unit can sustain a maximum of eight concurrent accesses to the array buffer.

Example 2

Using the same integer array as found in Example 1, block partitioning can be applied to the array with the following declaration:

int buffer[16] __attribute__((xcl_array_partition(block,4,1)));

Because the size of the block is four, the SDAccel compiler will generate four physical memories, sequentially filling each memory with data from the array.

Example 3

Using the same integer array as found in Example 1, complete partitioning can be applied to the array with the following declaration:

int buffer[16] __attribute__((xcl_array_partition(complete, 1)));

In this example, the array is completely partitioned into distributed RAM, or 16 independent registers in the programmable logic of the kernel. Because complete is the default, the same effect can also be accomplished with the following declaration:

int buffer[16] __attribute__((xcl_array_partition));

While this creates an implementation with the highest possible memory bandwidth, it is not suited to all applications. The way in which data is accessed by the kernel code through either constant or data dependent indexes affects the amount of supporting logic that the SDAccel compiler has to build around each register to ensure functional equivalence with the usage in the original code. As a general best practice guideline for the SDx environment, the complete partitioning attribute is best suited for arrays in which at least one dimension of the array is accessed through the use of constant indexes.

See Also

• xcl_array_reshape
• pragma HLS array_partition
• SDAccel Environment Profiling and Optimization Guide
• Vivado Design Suite User Guide: High-Level Synthesis (UG902)

Description

This attribute combines array partitioning with vertical array mapping.

The XCL_ARRAY_RESHAPE attribute combines the effect of XCL_ARRAY_PARTITION, breaking an array into smaller arrays, and concatenating elements of arrays by increasing bit-widths. This reduces the number of block RAM consumed while providing parallel access to the data. This attribute creates a new array with fewer elements but with greater bit-width, allowing more data to be accessed in a single clock cycle.

Given the following code:

void foo (...) {
int array1[N] __attribute__((xcl_array_reshape(block, 2, 1)));
int array2[N] __attribute__((xcl_array_reshape(cycle, 2, 1)));
int array3[N] __attribute__((xcl_array_reshape(complete, 1)));
...
}

The ARRAY_RESHAPE attribute transforms the arrays into the form shown in the following figure:

Figure: ARRAY_RESHAPE

Syntax

Place the attribute with the definition of the array variable:

__attribute__((xcl_array_reshape(<type>,<factor>, 
<dimension>)))

Where:

• <type>: Specifies one of the following partition types:
  • cyclic: Cyclic partitioning is the implementation of an array as a set of smaller physical memories that can be accessed simultaneously by the logic in the compute unit. The array is partitioned cyclically by putting one element into each memory before coming back to the first memory to repeat the cycle until the array is fully partitioned.
  • block: Block partitioning is the physical implementation of an array as a set of smaller memories that can be accessed simultaneously by the logic inside of the compute unit. In this case, each memory block is filled with elements from the array before moving on to the next memory.
  • complete: Complete partitioning decomposes the array into individual elements. For a one-dimensional array, this corresponds to resolving a memory into individual registers. The default <type> is complete.
• <factor>: For cyclic type partitioning, the <factor> specifies how many physical memories to partition the original array into in the kernel code. For Block type partitioning, the <factor> specifies the number of elements from the original array to store in each physical memory.
  IMPORTANT: For complete type partitioning, the <factor> should not be specified.
• <dimension>: Specifies which array dimension to partition. Specified as an integer from 1 to <N>. SDAccel environment supports arrays of <N> dimensions and can partition the array on any single dimension.

Example 1

Reshapes (partition and maps) an 8-bit array with 17 elements, AB[17], into a new 32-bit array with five elements using block mapping.

int AB[17] __attribute__((xcl_array_reshape(block,4,1)));

Example 2

Reshapes the two-dimensional array AB[6][4] into a new array of dimension [6][2], in which dimension 2 has twice the bit-width:

int AB[6][4] __attribute__((xcl_array_reshape(block,2,2)));

Example 3

Reshapes the three-dimensional 8-bit array, AB[4][2][2] in function foo, into a new single element array (a register), 128 bits wide (4*2*2*8):

int AB[4][2][2] __attribute__((xcl_array_reshape(complete,0)));

See Also

• xcl_array_partition
• pragma HLS array_reshape
• SDAccel Environment Profiling and Optimization Guide
• Vivado Design Suite User Guide: High-Level Synthesis (UG902)

Description

The XCL_LATENCY attribute specifies a minimum, or maximum latency value, or both, for the completion of functions, loops, and regions. Latency is defined as the number of clock cycles required to produce an output. Function or region latency is the number of clock cycles required for the code to compute all output values, and return. Loop latency is the number of cycles to execute all iterations of the loop. See "Performance Metrics Example" of Vivado Design Suite User Guide: High-Level Synthesis (UG902).

TheVivado High-Level Synthesis (HLS) tool always tries to minimize latency in the design. When the XCL_LATENCY attribute is specified, the tool behavior is as follows:

• When latency is greater than the minimum, or less than the maximum: The constraint is satisfied. No further optimizations are performed.
• When latency is less than the minimum: If the HLS tool can achieve less than the minimum specified latency, it extends the latency to the specified value, potentially increasing sharing.
• When latency is greater than the maximum: If the HLS tool cannot schedule within the maximum limit, it increases effort to achieve the specified constraint. If it still fails to meet the maximum latency, it issues a warning, and produces a design with the smallest achievable latency in excess of the maximum.

Syntax

__attribute__((xcl_latency(min, max)))

Where:

• <min>: Specifies the minimum latency for the function, loop, or region of code.
• <max>: Specifies the maximum latency for the function, loop, or region of code.

Example 1

The for loop in the test function is specified to have a minimum latency of 4 and a maximum latency of 8:

__kernel void test(__global float *A, __global float *B, __global float *C, int id) 
{
  for (unsigned int i = 0; i < id; i++)
__attribute__((xcl_latency(4, 12))) {
   C[id] = A[id] * B[id];
 }
}

See Also

• pragma HLS latency
• SDAccel Environment Profiling and Optimization Guide
• Vivado Design Suite User Guide: High-Level Synthesis (UG902)

Description

The XCL_LATENCY attribute specifies a minimum, or maximum latency value, or both, for the completion of functions, loops, and regions. Latency is defined as the number of clock cycles required to produce an output. Function or region latency is the number of clock cycles required for the code to compute all output values, and return. Loop latency is the number of cycles to execute all iterations of the loop. See "Performance Metrics Example" of Vivado Design Suite User Guide: High-Level Synthesis (UG902).

TheVivado High-Level Synthesis (HLS) tool always tries to minimize latency in the design. When the XCL_LATENCY attribute is specified, the tool behavior is as follows:

• When latency is greater than the minimum, or less than the maximum: The constraint is satisfied. No further optimizations are performed.
• When latency is less than the minimum: If the HLS tool can achieve less than the minimum specified latency, it extends the latency to the specified value, potentially increasing sharing.
• When latency is greater than the maximum: If the HLS tool cannot schedule within the maximum limit, it increases effort to achieve the specified constraint. If it still fails to meet the maximum latency, it issues a warning, and produces a design with the smallest achievable latency in excess of the maximum.

Syntax

__attribute__((xcl_latency(min, max)))

Where:

• <min>: Specifies the minimum latency for the function, loop, or region of code.
• <max>: Specifies the maximum latency for the function, loop, or region of code.

Example 1

The for loop in the test function is specified to have a minimum latency of 4 and a maximum latency of 8:

__kernel void test(__global float *A, __global float *B, __global float *C, int id) 
{
  for (unsigned int i = 0; i < id; i++)
__attribute__((xcl_latency(4, 12))) {
   C[id] = A[id] * B[id];
 }
}

See Also

• pragma HLS latency
• SDAccel Environment Profiling and Optimization Guide
• Vivado Design Suite User Guide: High-Level Synthesis (UG902)

Description

Use this attribute instead of REQD_WORK_GROUP_SIZE when you need to specify a larger kernel than the 4K size.

Extends the default maximum work group size supported in the SDx environment by the reqd_work_group_size attribute. SDx environment supports work size larger than 4096 with the XCL_MAX_WORK_GROUP_SIZE attribute.

Syntax

Place this attribute before the kernel definition, or before the primary function specified for the kernel:

__attribute__((xcl_max_work_group_size(<X>, <Y>, <Z>)))

Where:

• <X>, <Y>, <Z>: Specifies the ND range of the kernel. This represents each dimension of a three dimensional matrix specifying the size of the work-group for the kernel.

Example 1

Below is the kernel source code for an un-optimized adder. No attributes were specified for this design, other than the work size equal to the size of the matrices (for example, 64x64). That is, iterating over an entire workgroup will fully add the input matrices, a and b, and output the result. All three are global integer pointers, which means each value in the matrices is four bytes, and is stored in off-chip DDR global memory.

#define RANK 64
__kernel __attribute__ ((reqd_work_group_size(RANK, RANK, 1)))
void madd(__global int* a, __global int* b, __global int* output) {
int index = get_local_id(1)*get_local_size(0) + get_local_id(0);
output[index] = a[index] + b[index];
}

This local work size of (64, 64, 1) is the same as the global work size. It should be noted that this setting creates a total work size of 4096.

Any matrix larger than 64x64 would need to only use one dimension to define the work size. That is, a 128x128 matrix could be operated on by a kernel with a work size of (128, 1, 1), where each invocation operates on an entire row, or column of data.

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

Pipeline a work item to improve latency and throughput. Work item pipelining is the extension of loop pipelining to the kernel work group. This is necessary for maximizing kernel throughput and performance.

Syntax

Place the attribute in the OpenCL API source before the elements to pipeline:

__attribute__((xcl_pipeline_workitems))

Example 1

In order to handle the reqd_work_group_size attribute in the following example, SDAccel tool automatically inserts a loop nest to handle the three-dimensional characteristics of the ND range (3,1,1). As a result of the added loop nest, the execution profile of this kernel is like an unpipelined loop. Adding the XCL_PIPELINE_WORKITEMS attribute adds concurrency and improves the throughput of the code.

kernel
__attribute__ ((reqd_work_group_size(3,1,1)))
void foo(...)
{
...
__attribute__((xcl_pipeline_workitems)) {
int tid = get_global_id(0);
op_Read(tid);
op_Compute(tid);
op_Write(tid);
}
...
}

Example 2

The following example adds the work-item pipeline to the appropriate elements of the kernel:

__kernel __attribute__ ((reqd_work_group_size(8, 8, 1)))
void madd(__global int* a, __global int* b, __global int* output)
{
int rank = get_local_size(0);
__local unsigned int bufa[64];
__local unsigned int bufb[64];
__attribute__((xcl_pipeline_workitems)) {
int x = get_local_id(0);
int y = get_local_id(1);
bufa[x*rank + y] = a[x*rank + y];
bufb[x*rank + y] = b[x*rank + y];
}
barrier(CLK_LOCAL_MEM_FENCE);
__attribute__((xcl_pipeline_workitems)) {
int index = get_local_id(1)*rank + get_local_id(0);
output[index] = bufa[index] + bufb[index];
}
}

See Also

• pragma HLS pipeline
• SDAccel Environment Profiling and Optimization Guide
• Vivado Design Suite User Guide: High-Level Synthesis (UG902)

Description

The OpenCL framework 2.0 specification introduces a new memory object called pipe. A pipe stores data organized as a FIFO. Pipes can be used to stream data from one kernel to another inside the FPGA without having to use the external memory, which greatly improves the overall system latency.

pipe int p0 __attribute__((xcl_reqd_pipe_depth(512)));

Pipes can only be accessed using standard OpenCL read_pipe() and write_pipe() built-in functions in non-blocking mode, or using Xilinx extended read_pipe_block() and write_pipe_block() functions in blocking mode.

Pipe objects are not accessible from the host CPU. The status of pipes can be queried using OpenCL get_pipe_num_packets() and get_pipe_max_packets() built-in functions. See The OpenCL C Specification from Khronos OpenCL Working Group for more details on these built-in functions.

Syntax

This attribute must be assigned at the declaration of the pipe object:

pipe int <id> __attribute__((xcl_reqd_pipe_depth(<n>)));

Where:

• <id>: Specifies an identifier for the pipe, which must consist of lower-case alphanumerics. For example <infifo1> not <inFifo1>.
• <n>: Specifies the depth of the pipe. Valid depth values are 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768.

Examples

The following is the dataflow_pipes_ocl example from Xilinx GitHub that use pipes to pass data from one processing stage to the next using blocking read_pipe_block() and write_pipe_block() functions:

pipe int p0 __attribute__((xcl_reqd_pipe_depth(32)));
pipe int p1 __attribute__((xcl_reqd_pipe_depth(32)));
// Input Stage Kernel : Read Data from Global Memory and write into Pipe P0
kernel __attribute__ ((reqd_work_group_size(1, 1, 1)))
void input_stage(__global int *input, int size)
{
__attribute__((xcl_pipeline_loop))
mem_rd: for (int i = 0 ; i < size ; i++)
{
//blocking Write command to pipe P0
write_pipe_block(p0, &input[i]);
}
}
// Adder Stage Kernel: Read Input data from Pipe P0 and write the result
// into Pipe P1
kernel __attribute__ ((reqd_work_group_size(1, 1, 1)))
void adder_stage(int inc, int size)
{
__attribute__((xcl_pipeline_loop))
execute: for(int i = 0 ; i < size ; i++)
{
int input_data, output_data;
//blocking read command to Pipe P0
read_pipe_block(p0, &input_data);
output_data = input_data + inc;
//blocking write command to Pipe P1
write_pipe_block(p1, &output_data);
}
}
// Output Stage Kernel: Read result from Pipe P1 and write the result to 
// Global Memory
kernel __attribute__ ((reqd_work_group_size(1, 1, 1)))
void output_stage(__global int *output, int size)
{
__attribute__((xcl_pipeline_loop))
mem_wr: for (int i = 0 ; i < size ; i++)
{
//blocking read command to Pipe P1
read_pipe_block(p1, &output[i]);
}
}

See Also

• SDAccel Environment Profiling and Optimization Guide
• https://www.khronos.org/
• The OpenCL C Specification

Description

If you use clEnqueueNDRangeKernel with the global_work_offset set to NULL or all zeros, you can use this attribute to tell the compiler that the global_work_offset is always zero.

This attribute can improve memory performance when you have memory accesses like:

A[get_global_id(x)] = ...;

Syntax

Place this attribute before the kernel definition, or before the primary function specified for the kernel:

__kernel __attribute__((xcl_zero_global_work_offset))
void test (__global short *input, __global short *output, __constant short *constants) { }

See Also

• reqd_work_group_size
• vec_type_hint
• clEnqueueNDRangeKernel
• SDAccel Environment Profiling and Optimization Guide