c-examples
plb2
| c-examples | plb2 | |
|---|---|---|
| 4 | 7 | |
| 4 | 238 | |
| - | - | |
| 9.1 | 9.4 | |
| 19 days ago | 10 days ago | |
| C | C | |
| GNU General Public License v3.0 or later | Creative Commons Zero v1.0 Universal |
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c-examples
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Benchmarking 20 programming languages on N-queens and matrix multiplication
So I actually tested your code: https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6...
On my machine single-threaded OpenBLAS multiplies two single precision 4096x4096 matrices in 0.95 seconds. Your code takes over 30 seconds. For comparison, my own matrix multiplication code (https://github.com/bjourne/c-examples/blob/master/libraries/...) run in single-threaded mode takes 0.89 seconds. Which actually beats OpenBLAS, but OpenBLAS retakes the lead for larger arrays when multi-threading is added.
- Julia and Mojo (Modular) Mandelbrot Benchmark
- Reference Count, Don't Garbage Collect
plb2
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Byte-Sized Swift: Building Tiny Games for the Playdate
https://github.com/attractivechaos/plb2 - limited but broad comparison across a large number of languages. Swift and Nim both compare favourably to C.
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The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions
https://github.com/attractivechaos/plb2/blob/master/README.m...
Synthetic benchmarks aside, I think as far as average (spring boots of the world) code goes, Go beats Java almost every time, often in less lines than the usual pom.xml
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Python 3.13 Gets a JIT
I wouldn't be so enthusiastic. Look at other languages that have JIT now: Ruby and PHP. After years of efforts, they are still an order of magnitude slower than V8 and even PyPy [1]. It seems to me that you need to design a JIT implementation from ground up to get good performance – V8, Dart, LuaJIT and PyPy are like this; if you start with a pure interpreter, it may be difficult to speed it up later.
[1] https://github.com/attractivechaos/plb2
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Benchmarking 20 programming languages on N-queens and matrix multiplication
A curious thing about Swift: after https://github.com/attractivechaos/plb2/pull/23, the matrix multiplication example is comparable to C and Rust. However, I don’t see a way to idiomatically optimise the sudoku example, whose main overhead is allocating several arrays each time solve() is called. Apparently, in Swift there is no such thing as static array allocation. That’s very unfortunate.
What are some alternatives?
ixy-languages - A high-speed network driver written in C, Rust, C++, Go, C#, Java, OCaml, Haskell, Swift, Javascript, and Python
laser - The HPC toolbox: fused matrix multiplication, convolution, data-parallel strided tensor primitives, OpenMP facilities, SIMD, JIT Assembler, CPU detection, state-of-the-art vectorized BLAS for floats and integers
mark-sweep - A simple mark-sweep garbage collector in C
weave - A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead
.NET Runtime - .NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
tarantool - Get your data in RAM. Get compute close to data. Enjoy the performance.
racket - The Racket repository
blis - BLAS-like Library Instantiation Software Framework
Mesh - A memory allocator that automatically reduces the memory footprint of C/C++ applications.
related_post_gen - Data Processing benchmark featuring Rust, Go, Swift, Zig, Julia etc.
array - C++ multidimensional arrays in the spirit of the STL
1brc - 1BRC in .NET among fastest on Linux