Programming Parallel Computers

Chapter 4: GPU programming

Version 1: OpenCL

In the OpenCL version, the change is of course equally trivial:

const char* kernel_source =
__kernel void mykernel(__global float* r, __global const float* d, int n) {
    int i = get_global_id(0);
    int j = get_global_id(1);
    if (i >= n || j >= n)
    float v = HUGE_VALF;
    for (int k = 0; k < n; ++k) {
        float x = d[n*j + k];
        float y = d[n*k + i];
        float z = x + y;
        v = min(v, z);
    r[n*j + i] = v;

What is perhaps much more interesting is the fact that we get very similar performance improvements across a wide variety of platforms. Here are examples of the benchmarks of this code and the previous version (total running time for two iterations, input size n = 4000):

GPU:AMD Radeon R9 M390NVIDIA Quadro K2200NVIDIA Quadro K2200
V0:9.2 s14.9 s14.4 s
V1:1.9 s3.9 s3.6 s
speedup:4.9 ×3.8 ×4.0 ×

While the APIs are superficially different, and the technical details of the hardware differ a lot too, the same key principles related to the memory access patterns still hold.