If you wish to see some speedups using AVX512, without limiting yourself to C or C++, you might want to try ISPC (https://ispc.github.io/index.html).
You'll get sane aliasing rules from the perspective of performance, multi-target binaries with dynamic dispatching and a lot more control over the code generated.
> In CPU world there is a desire to shield programmers from those low-level details, but I think there are two interesting forces at play now-a-days that’ll change it soon. On one hand, Dennard Scaling (aka free lunch) is long gone, hardware landscape is getting increasingly fragmented and specialized out of necessity, software abstractions are getting leakier, forcing developers to be aware of the lowest levels of abstraction, hardware, for good performance.
The problem is that not all programming languages expose SIMD, and even if they do it is only a portable subset, additionally the kind of skills that are required to be able to use SIMD properly isn't something everyone is confortable doing.
I certainly am not, still managed to get around with MMX and early SSE, can manage shading languages, and that is about it.
The good news is that the portable subset of SIMD is all you really need anyway. If you go beyond the portable subset, you need per-architecture code writing and testing, and you're mostly talking about pretty small gains relative to the cost.
> The answer, if it’s not obvious from my tone already:), is 8%.
Not if the data is small and in cache.
> The performant route with AVX-512 would probably include the instruction vpconflictd, but I couldn’t really find any elegant way to use it.
I think the best way to do this is duplicate sum_r and count 16 times, so each pane has a seperate accumulation bucket and there can't be any conflicts. After the loop, you quickly do a sum reduction for each of the 16 buckets.
Yeah N is big enough that entire data isn't in the cache, but the memory access pattern here is the next best thing: totally linear, predictable access. I remember seeing around 94%+ L1d cache hit rate.
Initial example takes array pointers without the __restrict__ keyword/extension so compiler might assume they could be aliased to same address space and will code defensively.
Would be interesting to see if auto vec performs better with that addition.
which honestly, shouldn't be neccessary today with avx512. There's essentially no reason to prefer the aligned load/store commands over the unaligned ones - if the actual pointer is unaligned it will function correctly at half the throughput, while if it_is_ aligned you will get the same performance as the aligned-only load.
No reason for the compiler to balk at vectorizing unaligned data these days.
> There's essentially no reason to prefer the aligned load/store commands over the unaligned ones - if the actual pointer is unaligned it will function correctly at half the throughput
Getting a fault instead of half the performance is actually a really good reason to prefer aligned load/store. To be fair, you're talking about a compiler here, but I never understood why people use the unaligned intrinsics...
There are many situations where your data is essentially _majority_ unaligned. Considerable effort by the hardware guys has gone into making that situation work well.
A great example would be a convolution-kernel style code - with AVX512 you are using 64 bytes at a time (a whole cacheline), and sampling a +/- N neighborhood around a pixel. By definition most of those reads will be unaligned!
A lot of other great use cases for SIMD don't let you dictate the buffer alignment. If the code is relatively bandwidth limited, I have found it to be worth doing a head/body/tail situation where you do one misaligned iteration before doing the bulk of the work in alignment, but honestly for that to be worth it you have to be working almost completely out of L1 cache which is rare... otherwise you're going to be slowed down to L2 or memory speed at which point the half rate penalty doesn't really matter.
The early SSE-style instructions often favored making two aligned reads and then extracting your sliding window from that, but there's just no point doing that on modern hardware - it will be slower.
What I get in these article is that the original intent on C language stands true.
Use C as a common platform denominator without crazy optimizations (like tcc). If you need performance, specialize, C gives you the tools to call assembly (or use compiler some intrinsic or even inline assembly).
Complex compiler doing crazy optimizations, in my opinion, is not worth it.
> Complex compiler doing crazy optimizations, in my opinion, is not worth it.
For these optimisations that are in the back-end, they are used for other languages that can be higher-level or that cannot drop to assembler as easily. C is just one of the front-ends of modern compiler suites.
I found this a weird article.
If you wish to see some speedups using AVX512, without limiting yourself to C or C++, you might want to try ISPC (https://ispc.github.io/index.html).
You'll get sane aliasing rules from the perspective of performance, multi-target binaries with dynamic dispatching and a lot more control over the code generated.
> In CPU world there is a desire to shield programmers from those low-level details, but I think there are two interesting forces at play now-a-days that’ll change it soon. On one hand, Dennard Scaling (aka free lunch) is long gone, hardware landscape is getting increasingly fragmented and specialized out of necessity, software abstractions are getting leakier, forcing developers to be aware of the lowest levels of abstraction, hardware, for good performance.
The problem is that not all programming languages expose SIMD, and even if they do it is only a portable subset, additionally the kind of skills that are required to be able to use SIMD properly isn't something everyone is confortable doing.
I certainly am not, still managed to get around with MMX and early SSE, can manage shading languages, and that is about it.
The good news is that the portable subset of SIMD is all you really need anyway. If you go beyond the portable subset, you need per-architecture code writing and testing, and you're mostly talking about pretty small gains relative to the cost.
> The answer, if it’s not obvious from my tone already:), is 8%.
Not if the data is small and in cache.
> The performant route with AVX-512 would probably include the instruction vpconflictd, but I couldn’t really find any elegant way to use it.
I think the best way to do this is duplicate sum_r and count 16 times, so each pane has a seperate accumulation bucket and there can't be any conflicts. After the loop, you quickly do a sum reduction for each of the 16 buckets.
Yeah N is big enough that entire data isn't in the cache, but the memory access pattern here is the next best thing: totally linear, predictable access. I remember seeing around 94%+ L1d cache hit rate.
Initial example takes array pointers without the __restrict__ keyword/extension so compiler might assume they could be aliased to same address space and will code defensively.
Would be interesting to see if auto vec performs better with that addition.
Also trying to let the compilers know that the float* are aligned would be a good move.
auto aligned_p = std::assume_aligned<16>(p)
[delayed]
which honestly, shouldn't be neccessary today with avx512. There's essentially no reason to prefer the aligned load/store commands over the unaligned ones - if the actual pointer is unaligned it will function correctly at half the throughput, while if it_is_ aligned you will get the same performance as the aligned-only load.
No reason for the compiler to balk at vectorizing unaligned data these days.
> There's essentially no reason to prefer the aligned load/store commands over the unaligned ones - if the actual pointer is unaligned it will function correctly at half the throughput
Getting a fault instead of half the performance is actually a really good reason to prefer aligned load/store. To be fair, you're talking about a compiler here, but I never understood why people use the unaligned intrinsics...
There are many situations where your data is essentially _majority_ unaligned. Considerable effort by the hardware guys has gone into making that situation work well.
A great example would be a convolution-kernel style code - with AVX512 you are using 64 bytes at a time (a whole cacheline), and sampling a +/- N neighborhood around a pixel. By definition most of those reads will be unaligned!
A lot of other great use cases for SIMD don't let you dictate the buffer alignment. If the code is relatively bandwidth limited, I have found it to be worth doing a head/body/tail situation where you do one misaligned iteration before doing the bulk of the work in alignment, but honestly for that to be worth it you have to be working almost completely out of L1 cache which is rare... otherwise you're going to be slowed down to L2 or memory speed at which point the half rate penalty doesn't really matter.
The early SSE-style instructions often favored making two aligned reads and then extracting your sliding window from that, but there's just no point doing that on modern hardware - it will be slower.
What I get in these article is that the original intent on C language stands true.
Use C as a common platform denominator without crazy optimizations (like tcc). If you need performance, specialize, C gives you the tools to call assembly (or use compiler some intrinsic or even inline assembly).
Complex compiler doing crazy optimizations, in my opinion, is not worth it.
> Complex compiler doing crazy optimizations, in my opinion, is not worth it.
For these optimisations that are in the back-end, they are used for other languages that can be higher-level or that cannot drop to assembler as easily. C is just one of the front-ends of modern compiler suites.
Well, C is a lie anyway: it's not how computers work any more (and I'm not sure it's how they ever worked).
If you have the opportunity, try out a zen5. Significant improvements.
See also https://www.numberworld.org/blogs/2024_8_7_zen5_avx512_teard...