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Gemm-benchmark Alternatives
Similar projects and alternatives to gemm-benchmark
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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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XNNPACK
High-efficiency floating-point neural network inference operators for mobile, server, and Web
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OnnxStream
Lightweight inference library for ONNX files, written in C++. It can run SDXL on a RPI Zero 2 but also Mistral 7B on desktops and servers.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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armnn
Arm NN ML Software. The code here is a read-only mirror of https://review.mlplatform.org/admin/repos/ml/armnn
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better gemm-benchmark alternative or higher similarity.
gemm-benchmark reviews and mentions
Posts with mentions or reviews of gemm-benchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-20.
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Running Stable Diffusion in 260MB of RAM
And PyTorch on the M1 (without Metal) uses the fast AMX matrix multiplication units (through the Accelerate Framework). The matrix multiplication on the M1 is on par with ~10 threads/cores of Ryzen 5900X.
[1] https://github.com/danieldk/gemm-benchmark#example-results
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Ask HN: What is a AI chip and how does it work?
Apple Silicon Macs have special matrix multiplication units (AMX) that can do matrix multiplication fast and with low energy requirements [1]. These AMX units can often beat matrix multiplication on AMD/Intel CPUs (especially those without a very large number of cores). Since a lot of linear algebra code uses matrix multiplication and using the AMX units is only a matter of linking against Accelerate (for its BLAS interface), a lot of software that uses BLAS is faster o Apple Silicon Macs.
That said, the GPUs in your M1 Mac are faster than the AMX units and any reasonably modern NVIDIA GPU will wipe the floor with the AMX units or Apple Silicon GPUs in raw compute. However, a lot of software does not use CUDA by default and for small problem sets AMX units or CPUs with just AVX can be faster because they don't incur the cost of data transfers from main memory to GPU memory and vice versa.
[1] Benchmarks:
https://github.com/danieldk/gemm-benchmark#example-results
https://explosion.ai/blog/metal-performance-shaders (scroll down a bit for AMX and MPS numbers)
- Apple previews Live Speech, Personal Voice, and more new accessibility features
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How to Get 1.5 TFlops of FP32 Performance on a Single M1 CPU Core
Yes, there is one per core cluster. The title is a bit misleading, because it suggests that going to two or three cores will scale linearly, though it won't be much faster. See here for sgemm benchmarks for everything from the M1 to M1 Ultra and 1 to 16 threads:
https://github.com/danieldk/gemm-benchmark#1-to-16-threads
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WebAssembly Techniques to Speed Up Matrix Multiplication by 120x
There's always been a tradeoff in writing code between developer experience and taking full advantage of what the hardware is capable of. That "waste" in execution efficiency is often worth it for the sake of representing helpful abstractions and generally helping developer productivity.
The GFLOP/s is 1/28th of what you'd get when using the native Accelerate framework on M1 Macs [1]. I am all in for powerful abstractions, but not using native APIs for this (even if it's just the browser calling Accelerate in some way) is just a huge waste of everyone's CPU cycles and electricity.
[1] https://github.com/danieldk/gemm-benchmark#1-to-16-threads
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A note from our sponsor - InfluxDB
www.influxdata.com | 2 May 2024
Stats
Basic gemm-benchmark repo stats
6
8
3.5
6 months ago
danieldk/gemm-benchmark is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of gemm-benchmark is Rust.
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