avo VS laser

I wonder whether avo could have been useful here?[1] I mention it because it came up the last time we were talking about AVX operations in go.[2]

1 = https://github.com/mmcloughlin/avo

2 = https://news.ycombinator.com/item?id=34465297

I thought the /r/golang comments on this post were pretty useful[1]. They also introduced me to avo[2], a tool for generating x86 assembly from go that I hadn't seen before. There are some examples listed on the avo github page for generating AVX512 instructions with avo.

1 = https://www.reddit.com/r/golang/comments/10hmh07/how_to_use_...

2 = https://github.com/mmcloughlin/avo

For writing AVX512 from scratch avo is a much better alternative.

Avo is a library that simplifies writing complex go assembly, I found it very useful to figure out how instructions map onto Go's asm syntax. But you could definitely do the translation directly, it's what c2goasm did (couldn't get it to work reliably unfortunately).

Curious if you're looking at using avo to write the assembly

You can use github.com/mmcloughlin/avo for generating the assembly use Go.

For normal and "better" mode I am using avo to generate different encoders for different input sizes, with and without Snappy compatibility. That currently outputs about 17k lines of assembly.

You could perhaps just have the Go compiler generate the assembler for your code:

go tool compile -S file.go > file_amd64.s

Then you could verify it doesn't change over time, and choose to begin maintaining by hand if it makes sense.

If you do want to go the route of rolling it yourself, I'd suggest looking into something like Avo: https://github.com/mmcloughlin/avo

If you have to go with Assembly, try Avo https://github.com/mmcloughlin/avo

It depends.

You need 2~3 accumulators to saturate instruction-level parallelism with a parallel sum reduction. But the compiler won't do it because it only creates those when the operation is associative, i.e. (a+b)+c = a+(b+c), which is true for integers but not for floats.

There is an escape hatch in -ffast-math.

I have extensive benches on this here: https://github.com/mratsim/laser/blob/master/benchmarks%2Ffp...

Ah,

It was from an older implementation that wasn't compatible with Nim v2. I've commented it out.

If you pull again it should work.

> Anyway the reason for your competitive performance is likely that you are benchmarking with very small matrices. OpenBLAS spends some time preprocessing the tiles which doesn't really pay off until they become really huge.

I don't get why you think it's impossible to reach BLAS speed. The matrix sizes are configured here: https://github.com/mratsim/laser/blob/master/benchmarks/gemm...

It defaults to 1920x1920 * 1920x1920. Note, if you activate the benchmarks versus PyTorch Glow, in the past it didn't support non-multiple of 16 or something, not sure today.

Packing is done here: https://github.com/mratsim/laser/blob/master/laser/primitive...

And it also support pre-packing which is useful to reimplement batch_matmul like what CuBLAS provides and is quite useful for convolution via matmul.

For convolutions: - https://github.com/numforge/laser/blob/e23b5d63/research/convolution_optimisation_resources.md

You can train yourself on matrix transposition first. It's straightforward to get 3x speedup between naive transposition and double loop tiling, see: https://github.com/numforge/laser/blob/d1e6ae6/benchmarks/transpose/transpose_bench.nim#L238