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Laser Alternatives
Similar projects and alternatives to laser
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Apache Arrow
Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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
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1brc
1️⃣🐝🏎️ The One Billion Row Challenge -- A fun exploration of how quickly 1B rows from a text file can be aggregated with Java
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SimSIMD
Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
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ParallelReductionsBenchmark
Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!
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weave
A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead (by mratsim)
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JohnTheRipper
Discontinued 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]
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better laser alternative or higher similarity.
laser reviews and mentions
Posts with mentions or reviews of laser.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-23.
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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
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www.saashub.com | 2 May 2024
Stats
Basic laser repo stats
6
261
3.6
4 months ago
mratsim/laser is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of laser is Nim.
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