• 10/30/2018
  • Public Content
Contents

Prefer Row-Wise Data Accesses

OpenCL™ enables you to submit kernels on one-, two-, or three-dimensional index space. Consider using one-dimensional ranges for reasons of cache locality and saving index computations.
If two- or three-dimensional range naturally fits your data dimensions, try to keep work-items scanning along rows, not columns. For example, the following code is not optimized (it might trigger gathers instructions):
__kernel void smooth(__constant float* input,                      uint image_width, uint image_height,                      __global float* output) {   int myX = get_global_id(1);   int myY = get_global_id(0);   int myPixel = myY * image_width + myX;   float data = input[myPixel];   … }
In this code example, the image height is the first dimension and the image width is the second dimension. The resulting column-wise data access is inefficient, since Intel® OpenCL™ implementation initially iterates over the first dimension.
Below is more optimal version, because of more memory-friendly (sequential) access.
__kernel void smooth(__constant float* input,                      uint image_width, uint image_height,                      __global float* output) {   int myX = get_global_id(0);   int myY = get_global_id(1);   int myPixel = myY * image_width + myX;   float data = input[myPixel];   … }
In the example above, the first dimension is the image width and the second is the image height.
The same rule applies if each work-item calculates several elements. To optimize performance, make sure work-items read from consecutive memory addresses.
Finally, if you run two-dimensional NDRange, prefer the data access to be consecutive along dimension zero.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.