unimore/hpc-2022-g3
unimore/hpc-2022-g3
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Aggiunta una reduction (al momento fa poco, magari con acceleratore va meglio), tolto un *4 perche Jetson ha 4 core CPU)

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FABIO ZANICHELLI 2022-11-16 15:01:58 -05:00
parent d89c501b59
commit 9dc24a3367
1 changed files with 6 additions and 2 deletions
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8
OpenMP/linear-algebra/kernels/atax/atax.c
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@ -32,7 +32,6 @@ static void init_array(int nx, int ny,
/// Initialize the `A` matrix with [something?]
// Using 4 threads here slows everything down: why?
// #pragma omp parallel for num_threads(4) schedule(static)
for (i = 0; i < nx; i++)
for (j = 0; j < ny; j++)
A[i][j] = ((DATA_TYPE)i * (j + 1)) / nx;
@ -71,12 +70,13 @@ static void kernel_atax(int nx, int ny,
/// This computes... something? I guess whatever ATAX is?
// Now this gives a nice speedup, especially with a lot more threads than the count!
// THREAD_COUNT * 4 seems to be the best on my local computer. What's the best for the Jetson Nano?
#pragma omp parallel for num_threads(THREAD_COUNT * 4) schedule(static)
#pragma omp parallel for num_threads(THREAD_COUNT) schedule(static)
for (i = 0; i < _PB_NX; i++)
{
/// Every iteration has its own tmp variable
DATA_TYPE tmp = 0;
#pragma omp parallel for num_threads(THREAD_COUNT) reduction(+:tmp)
for (j = 0; j < _PB_NY; j++)
/// Which gets increased by a bit on every iteration
tmp += A[i][j] * x[j];
@ -98,11 +98,15 @@ int main(int argc, char **argv)
POLYBENCH_1D_ARRAY_DECL(x, DATA_TYPE, NY, ny);
POLYBENCH_1D_ARRAY_DECL(y, DATA_TYPE, NY, ny);
/* Initialize array(s). */
init_array(nx, ny, POLYBENCH_ARRAY(A), POLYBENCH_ARRAY(x));
/* Start timer. */
polybench_start_instruments;
// polybench_start_instruments;
/* Run kernel. */
kernel_atax(nx, ny,