1brc
laser
1brc | laser | |
---|---|---|
28 | 6 | |
5,246 | 262 | |
- | 1.9% | |
9.8 | 3.6 | |
27 days ago | 5 months ago | |
Java | Nim | |
Apache License 2.0 | Apache License 2.0 |
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1brc
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The One Billion Row Challenge in CUDA: from 17 minutes to 17 seconds
This would be the code to beat. Ideally with only 8 cores but any number of cores is also very interesting.
https://github.com/gunnarmorling/1brc/discussions/710
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One Billion Row Challenge in Golang - From 95s to 1.96s
Given that 1-billion-line-file is approximately 13GB, instead of providing a fixed database, the official repository offers a script to generate synthetic data with random readings. Just follow the instructions to create your own database.
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1BRC Merykitty's Magic SWAR: 8 Lines of Code Explained in 3k Words
Local disk I/O is no longer the bottleneck on modern systems: https://benhoyt.com/writings/io-is-no-longer-the-bottleneck/
In addition, the official 1BRC explicitly evaluated results on a RAM disk to avoid I/O speed entirely: https://github.com/gunnarmorling/1brc?tab=readme-ov-file#eva... "Programs are run from a RAM disk (i.o. the IO overhead for loading the file from disk is not relevant)"
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Processing One Billion Rows in PHP!
You may have heard of the "The One Billion Row Challenge" (1brc) and in case you don't, go checkout Gunnar Morlings's 1brc repo.
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The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions
Here’s a thread on results with duckdb, I don’t mean to discourage you taking a shot at all though: https://github.com/gunnarmorling/1brc/discussions/39
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Ask HN: How can I learn about performance optimization?
If you are in “javaland” look at billion row challenge, you will learn a lot - https://github.com/gunnarmorling/1brc
- Lessons Learned from Doing the One Billion Row Challenge
- 1B Row Challenge Shows Java Can Process 1B Rows File in 2 Seconds
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From slow to SIMD: A Go optimization story
Even manual vectorization is pain...writing ASM, really?
Rust has unstable portable SIMD and a few third-party crates, C++ has that as well, C# has stable portable SIMD and a very small BLAS-like library on top of it (hell it even exercises PackedSIMD when ran in a browser) and Java is getting stable Panama vectors some time in the future (though the question of codegen quality stands open given planned changes to unsafe API).
Go among these is uniquely disadvantaged. And if that's not enough, you may want to visit 1Brc's challenge discussions and see that Go struggles get anywhere close to 2s mark with both C# and C++ are blazing past it:
https://hotforknowledge.com/2024/01/13/1brc-in-dotnet-among-...
https://github.com/gunnarmorling/1brc/discussions/67
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JEP Draft: Deprecate Memory-Access Methods in Sun.misc.Unsafe for Removal
In terms of performance: I realize that this is a somewhat "toy" issue, and it's a sample size of 1, but for the currently ongoing "One Billion Row Challenge"[1] (an ongoing Java performance competition related to parsing and aggregating a 13 GB file), all of the current top-performers are using Unsafe. More specifically, the use of Unsafe appears to have been the change for a few entries that allowed getting below the 3-second barrier in the test.
1. https://github.com/gunnarmorling/1brc
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?
1brc - C99 implementation of the 1 Billion Rows Challenge. 1️⃣🐝🏎️ Runs in ~1.6 seconds on my not-so-fast laptop CPU w/ 16GB RAM.
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
yolov7-object-tracking - YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking
nim-sos - Nim wrapper for Sandia-OpenSHMEM
csvlens - Command line csv viewer
ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!
nodejs - 1️⃣🐝🏎️ The One Billion Row Challenge with Node.js -- A fun exploration of how quickly 1B rows from a text file can be aggregated with different languages.
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
pocketbase - Open Source realtime backend in 1 file
blis - BLAS-like Library Instantiation Software Framework
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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]