You can either have performance (=write manual ASM for each platform), or portability, but not both.
What so-called "portable SIMD" libraries give you is "portable auto-vectorization". "Portable performance" is a global property of the algorithm. Relying on auto-vectorization will result in e.g. sub-optimal register spills in practice. The microbenchmarks will look great, though. ;)
Getting 2x or 4x performance in your inner loops using a reasonable SIMD library is infinitely better than theoretically getting 8x performance with hand-coded nonportable intrinsics, because the latter is never going to happen in most programs, so the actual point of comparison is scalar code, or autovectorized code at best.
Hot take: there is no portable SIMD.
You can either have performance (=write manual ASM for each platform), or portability, but not both.
What so-called "portable SIMD" libraries give you is "portable auto-vectorization". "Portable performance" is a global property of the algorithm. Relying on auto-vectorization will result in e.g. sub-optimal register spills in practice. The microbenchmarks will look great, though. ;)
Getting 2x or 4x performance in your inner loops using a reasonable SIMD library is infinitely better than theoretically getting 8x performance with hand-coded nonportable intrinsics, because the latter is never going to happen in most programs, so the actual point of comparison is scalar code, or autovectorized code at best.
> there is no portable SIMD
Except in languages with a JIT compiler
What about numpy, numba, and torch.compile?