Alternatives
Products that do what FFTN, faster than FFTW in 700 lines of C does
I am playing around with using arrays of arbitrary dimension as framework for designing FFT implementations, as opposed to the more classical approach of tensor products and butterflies (too complicated in my opinion). It turns out, that with a modern compiler, you do not need much complexity to make a really fast implementation. This implementation is for powers of 2, and optimized for arrays that do not fit in cache. I do think it would be better to use a higher-level language to implement other cases (e.g. n = 2^a * 3^b * 5^c, multiple small FFTs, higher-dimensional), so I am currently…
- 1SU
Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org
- 2S2
Had some fun with shared array buffers over many months of free time. Skip to the end to play around with the final app. Open to ideas on how to simulate more whilst staying in js land.
2024 · dgerrells.com
- 3MM
I've been working on implementing the compile-time approach to memory management described in this thesis (https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-908.pdf) for some time now - some of the performance results look promising! (Although some less so...) I think it would be great to see this taken further and built into a more complete functional language.
2020 · github.com
- 4AM
2018 · lambdaway.free.fr
- 5MT
2013 · teamten.com
- 6AH
Long-simmering side project that is finally ready to see the light. HAMTs are a cool persistent data structure and implementing one has been a lot of fun. Beyond the code, there is likely some value in the extensive and largely complete implementation docs; basic benchmarks are linked in the README, too. Kind of aiming to be "the libavl for HAMTs". That is obviously a high and aspirational bar but a distinct possibility if it stirs up a little interest and/or contribution. Anyways, it's time for this to go out, collect feedback and maybe even some use outside of toy projects. Let me…
2023 · github.com
- 7
A new programming language rooted in Pure Mathematics
2025
- 8FS
I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library. The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware.…
2023 · ashvardanian.com
- 9FA
2021 · github.com
- 10LO
Hi HN, I’m Joe. My friends Matthew, Jake and I are building Luminal (https://luminalai.com/), a GPU compiler for automatically generating fast GPU kernels for AI models. It uses search-based compilation to achieve high performance. We take high level model code, like you'd have in PyTorch, and generate very fast GPU code. We do that without using LLMs or AI - rather, we pose it as a search problem. Our compiler builds a search space, generates millions of possible kernels, and then searches through it to minimize runtime. You can try out a demo in `demos/matmul` on mac to…
2025 · github.com
- 11LF
LoopMix128 is a fast C PRNG I wrote for non-cryptographic tasks. GitHub (MIT): https://github.com/danielcota/LoopMix128 Highlights: * ~0.37 ns/value (GCC 11.4, -O3 -march=native), 98% faster than xoroshiro128++ and PCG64. * Passes TestU01 BigCrush & PractRand (32TB). * Guaranteed 2^128 period. * Proven injective (192-bit state) via Z3 SMT solver; allows parallel streams. * Core requires only stdint.h. Seeking feedback on design, use cases, or further testing.
2025 · github.com
- 12IB
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com
- 13SA
* https://sparser.io * https://github.com/Unibeautify/sparser This is my attempt at creating a universal language parser. It attempts to solve a couple of problems: * Support multiple languages * Recursively extend support to languages embedded within other languages * Output a uniform format for all supported languages This is a personal project so any feedback would be helpful. Something interesting I found after I built it is that this parser is not as fast to write output as many other JavaScript parsers, but its output is much faster to read from due to the…
2019
- 14LR
I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that "it's over", an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless. Anyway, here is the project: https://github.com/bggb7781-collab/lrnnsmdds/tree/main couple of notes: 1. single C file, no dependencies. Below are literally all the "dependencies", not even custom header (copy paste from the top of the single c file): #define _POSIX_C_SOURCE 200809L #include #include…
Apr 2026 · raw.githubusercontent.com
- 15CI
Author: Danison Nuñez Project: Coderive Project Type: Programming Language Highlight: Formula-based Execution ## Coderive - Iterating Through 1 Quintillion in a Loop *Subtitle: How a phone interpreter achieves what supercomputers cannot* The Impossible Loop: ```java // In any other language, this would be computational suicide for i in [0 to 1Qi] { // 1,000,000,000,000,000,000 iterations arr[i] = i * i } ``` Traditional Reality: · Python: MemoryError at array creation · Java/C++: Theoretical 31 years (with 8 exabytes of RAM) · NumPy/TensorFlow: Immediate crash ·…
Dec 2025 · github.com
- 1624
2023 · gist.github.com
- 17FR
Morning HN. Random number generation feels is a somewhat underrepresented topic in the C++ realm. There is a lot of questionable info about it found online and even the standard library is quite behind the times in terms of it's algorithms. It suffers from trying to accommodate sometimes impractical standard requirements and has several ways of getting significantly bad statistical results. This leaves a lot easily achievable performance & quality on the table. So, being a mathematician who mostly works with stochastic models and wants these models to run fast and well, I embarked on a…
2025 · github.com
- 18LS
Earlier this year, I took a month to reexamine my coding habits and rethink some past design choices. I hope to rewrite and improve my FOSS libraries this year, and I needed answers to a few questions first. Perhaps some of these questions will resonate with others in the community, too. - Are coroutines viable for high-performance work? - Should I use SIMD intrinsics for clarity or drop to assembly for easier library distribution? - Has hardware caught up with vectorized scatter/gather in AVX-512 & SVE? - How do secure enclaves & pointer tagging differ on Intel, Arm, & AMD? - What's…
2025 · github.com
- 19AS
Took a stab at building my own string diffing JS package. I built an interactive demo for TypeIt’s website (https://typeitjs.com/build-your-own) a while back. The approach I took to handle user input necessitated a way to calculate the difference between the versions of a user’s text input. I searched around for a package to help me out and found a couple of good ones (like fast-diff), but I either didn’t really like their API or didn’t want to take on a huge new dependency. Instead, I thought I’d give it a shot myself (famous last words). I dove into it having no real formal…
2022
- 20LA
tl;dr I'm developing an incredibly fast library for arrays and mathematics, and I've implemented a few new features and made some improvements. I'd love for you to check it out! Links: GitHub: https://github.com/LibRapid/librapid/ Documentation: https://librapid.readthedocs.io/en/latest/ Discord: https://discord.com/invite/cGxTFTgCAC Hey everyone! I am the lead developer of LibRapid (https://github.com/LibRapid/librapid/), a high-performance C++ library for array manipulation and mathematics. I've…
2023
- 21FA
Hello HN! I want to introduce fastgron, my new project. fastgron is a JSON to GRON converter, built to be incredibly fast – it's 40 times faster than Gron. GRON is a tool for making JSON greppable, but it can slow down with larger files. With fastgron, even a 200MB JSON file can be converted in just 1 second. Key features include streaming conversion for memory efficiency and an optimized path reconstruction for faster operations. It leverages C++ and the simdjson and fast_io libraries for speed. I welcome all feedback, suggestions, or questions. Thank you!
2023 · github.com
- 22RR
Hi, I’m Nabeel. In August I released RunMat as an open-source runtime for MATLAB code that was already much faster than GNU Octave on the workloads I tried. https://news.ycombinator.com/item?id=44972919 Since then, I’ve taken it further with RunMat Accelerate: the runtime now automatically fuses operations and routes work between CPU and GPU. You write MATLAB-style code, and RunMat runs your computation across CPUs and GPUs for speed. No CUDA, no kernel code. Under the hood, it builds a graph of your array math, fuses long chains into a few kernels, keeps data on the GPU when…
Dec 2025 · github.com
- 23
"I wanted to see if I could optimize the dequantization bottleneck during 4-bit LLM inference. By writing a custom kernel in Triton to optimize memory access patterns, I managed to get up to a 1.41x speedup over the standard bitsandbytes implementation. Check out the source code and benchmarks, feedback is highly appreciated!"
Jul 2026 · github.com
- 24RG
I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev
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