Hi,
Shown below is the code snippet of my program implemented using openmp parallel_for and using SYCL libraries(offload platform: CPU)
Case 1: Parallelization using openmp (standard cpp codes)
#pragma omp parallel for
for (int i = 0; i < loopCount; i++)
{
for (int m = 0; m < Len; m++)
{
OutputParallel[i] += testData[i + m] * Coeff[m];
}
}
case 2: Using DPC++ libraries; targets CPU
// buffer declaration
sycl::buffer testBuffx64(testData);
sycl::bufferBuffx64(Coeff);
sycl::buffer convBuffx64(Outputx64Oneapi);
t4 = std::chrono::high_resolution_clock::now();
try
{
cl::sycl::queue Q(sycl::cpu_selector{}); // device for computation offload
Q.submit([&](sycl::handler& h)
{
// accessor declaration
sycl::accessor testAccess(testBuffx64, h, sycl::read_only);
sycl::accessor Access(Buffx64, h, sycl::read_only);
sycl::accessor cAccess(convBuffx64, h, sycl::read_write);
h.parallel_for(sycl::range{ loopCount }, [=](sycl::id<1> idx) {
for (int m = 0; m < filterLen; m++)
{
cAccess[idx] += testAccess[idx + m] * Access[m];
}
});
});
Q.wait_and_throw();
}
catch (sycl::exception const& e)
{
std::cout << "Caught a SYCL host exception for x64:\n" << e.what() << "\n";
}
The throughput of case 2 is higher than case 1 although both are implemented on CPU.
Is there a reason why the DPC++ implementation is faster?
Thank you
