anematode
Joined 2 years ago
Joined 2 years ago
Maybe what's happened is that their bots reward hacked their way into special-casing the benchmarks without a proper audit from the humans involved, and now they're doing vibed damage control....
The (unsolicited) slop PRs on that repository are a sight to behold, though. The same slopper posted an aggressive response to my PR: https://github.com/Netflix/vmaf/pull/1586
... Oh by the way, did you know that Claude Code is actually a mini game engine? /s
for something like LLM inference, having this kind of abstraction seems like quite a hindrance
For example, you'll notice here that we perform a full vector store of 8 elements unconditionally, even if only a few of the elements are active. This is safe, though, because the output buffer is as large as the input buffer, and we're chunking by 8, so we'll never trash memory past the end; but this is a tricky analysis. Performance-wise, we rely on the CPU's store buffer to make these overlapping stores cheap.
Instead, you might think that you could just store the elements which are actually active, using a masked store. In fact there is also an intrinsic for this purpose (_mm512_mask_compressstoreu_pd), but it is extremely slow on some CPUs, namely Zen 4, so it's dangerous to use unless you know exactly what CPU you're using. (In my testing, there also seems to be some weird hazard on Zen 5 where multiple memory-destination compress instructions to nearby, even non-overlapping, addresses are serialized. But I haven't looked closer at this.)
You can do even a bit better if you're willing to use intrinsics. In particular this kind of operation is well-suited for compress-type operations, available as a first-class operation in at least AVX512, SVE and RVV; you can also emulate them reasonably quickly on NEON and AVX2.
Here's an example, building on the OP's work:
pub fn filter_compress(input: &[f64], threshold: f64) -> Vec<f64> {
use std::arch::x86_64::*;
let mut out = vec![0.0; input.len()];
let mut n = 0usize;
let (head, tail) = input.as_chunks::<8>();
for chunk in head {
unsafe {
let p = _mm512_loadu_pd(chunk.as_ptr());
let m = _mm512_cmpnle_pd_mask(p, _mm512_set1_pd(threshold));
let compress = _mm512_maskz_compress_pd(m, p);
_mm512_storeu_pd(out.as_mut_ptr().wrapping_add(n), compress);
n += m.count_ones() as usize;
}
}
for &x in tail {
out[n] = x;
n += (x > threshold) as usize;
}
out.truncate(n);
out
}
For me it's about 25% less time than the branchless version with 1,000,000 elements, and 60% less with 10,000 elements where memory bandwidth effects are less relevant.