I am tuning a population genetics simulation, and after replacing the two
main computational kernels (formerly intrinsics and SLEEF) with ISPC code,
my reference benchmark performance regressed from ~4.5s to ~19s.
Here is the ISPC code:
// Update the frequencies performing n *= exp(w * growth_factor) on count
elements
export uniform double update_frequencies_sum_n(
uniform uint len,
uniform double n_arr[],
uniform const double w_arr[],
uniform double growth_factor
) {
double sum_n = 0;
foreach (i = 0 ... len) {
double temp = w_arr[i] * growth_factor;
temp = exp(temp);
n_arr[i] *= temp;
sum_n += n_arr[i];
}
return reduce_add(sum_n);
}
/// Normalize n to sum of 1 and sum w weighted by n
export uniform double renormalize_n_weighted_sum_w(
uniform uint len,
uniform double n_arr[],
uniform const double w_arr[],
uniform double sum_n
) {
double sum_w = 0;
foreach (i = 0 ... len) {
n_arr[i] /= sum_n;
sum_w += n_arr[i] * w_arr[i];
}
return reduce_add(sum_w);
}
Both this and the original kernels with SLEEF + intrinsics were using AVX2
(i64x4 settings for ISPC/SLEEF). The original kernels included a scalar
peel loop for alignment, a pure vector loop, and a scalar remainder loop to
process extra elements.
I noticed that the performance difference was much milder if using floats
instead of doubles for the data being processed (~1.2s original kernel
switched to floats, ~2s ISPC) but the higher precision is needed here and
the performance is still much worse with the ISPC code.
Is there some performance optimization I'm missing with this ISPC code?
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