ParallelReductionsBenchmark
laser
| ParallelReductionsBenchmark | laser | |
|---|---|---|
| 2 | 6 | |
| 59 | 261 | |
| - | 1.5% | |
| 4.6 | 3.6 | |
| 5 months ago | 4 months ago | |
| C++ | Nim | |
| - | Apache License 2.0 |
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ParallelReductionsBenchmark
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Failing to Reach 204 GB/S DDR4 Bandwidth
For the single threaded version, they have a data hazard on the sums that could be smoothed out with a little loop unrolling and separate variables.
But in the [threaded version](https://github.com/unum-cloud/ParallelReductions/blob/fd16d9...) they have separate slots for an accumulator but it's still in a shared vector, which most likely has the issue I described.
laser
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From slow to SIMD: A Go optimization story
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...
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Benchmarking 20 programming languages on N-queens and matrix multiplication
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.
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Why does working with a transposed tensor not make the following operations less performant?
For convolutions: - https://github.com/numforge/laser/blob/e23b5d63/research/convolution_optimisation_resources.md
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Improve performance with SIMD intrinsics
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
What are some alternatives?
MatX - An efficient C++17 GPU numerical computing library with Python-like syntax
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
ispc - Intel® Implicit SPMD Program Compiler
nim-sos - Nim wrapper for Sandia-OpenSHMEM
gpuowl - GPU Mersenne primality test.
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
alpaka - Abstraction Library for Parallel Kernel Acceleration :llama:
blis - BLAS-like Library Instantiation Software Framework
cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:
JohnTheRipper - John the Ripper jumbo - advanced offline password cracker, which supports hundreds of hash and cipher types, and runs on many operating systems, CPUs, GPUs, and even some FPGAs [Moved to: https://github.com/openwall/john]
eaminer - Heterogeneous Ethereum Miner with support for AMD, Intel and Nvidia GPUs using SYCL, OpenCL and CUDA backends
john - John the Ripper jumbo - advanced offline password cracker, which supports hundreds of hash and cipher types, and runs on many operating systems, CPUs, GPUs, and even some FPGAs