Alternatives
Products that do what GPU-Based Autorouting for KiCad does
This project began when I decided it would be easier to write an autorouter than route a 8000+ net backplane by hand. This is a KiCad plugin with a few different algorithms, the coolest of which is a 'Manhattan routing grid' autorouter that routes along orthogonal traces. The basic idea was to steal an algorithm from FPGA routing and apply it to PCBs. I'm using CuPy for speeding up the routing; CPU-bound is at least 10x slower than the GPU version. This is in a very pre-alpha state, but it does _technically_ work. It's not great by any measure but then again it is an autorouter. I have a…
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- 6PB
2021 · github.com
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Hi HN! This is a little project to generate PCB footprints from text descriptions. It works by teaching an LLM a compressed DSL representation for footprints. I think this is generally a good technique if you're trying to get LLMs to work on spatial problems. Right now I'm focused on reproducing footprint shapes, but after that I'll benchmark the correctness of the footprints using % overlap on known package descriptions, as well as a kicad_mod export I built this because I realized I needed to use AI to help tscircuit catch up to the decades of electronic design tool progress that I'm not…
2024 · text-to-footprint.tscircuit.com
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Have you ever lost your mind at Desmos or Geogebra for occasionally being so god-damn slow and laggy? I have and, as a result, decided to make my own, GPU-accelerated (webgl2) graph plotter that renders equations, shaded plots, etc. with butter-smooth framerates. Equations we are used to think as unplottable (with usable real-time performance), like for example `e ^ (sin(x) * x ^ 2 * cos(y)) = cos(e ^ (x * y))`, will render just as smoothly [0] as something simple, like `y = x ^ 2`. Goals: * Rendering (almost) any equation without performance problems * Enabling acquiring new insights from…
2023 · kaapporaivio.fi
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2018 · github.com
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We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell:…
2018
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Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com
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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
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Hi HN! I've been hacking on this side project for the last month or two with the goal of making it dead simple to use cloud GPUs. I ran into this problem personally during the phd, and built my own tooling around it. I always thought it'd be fun to try to turn that tooling into a more general product... and bitbop.io is the result! All you have to do is run `ssh bitbop.io`, and you get your own personal dev GPU workstation in the cloud. Looking forward to hearing your thoughts!
2024 · twitter.com
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This is a personal project I've been working on and off for the past few years. It's a set of tools that have allowed me to prototype quickly small (and increasingly bigger) kernels, and create userspace programs to interact with them. Supports riscv64, amd64 and i386. The intel port has been tested and used on real hardware, from big dual-socket Xeon machines to an old X220. It is all C, I have plan to make rust bindings for the kernel library. But again, they're plans at this stage. Porting to new architectures is relatively simple, a basic port to riscv took me a couple of weeks of…
2024 · github.com
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I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal", "A100") cuda_device = machine.device("cuda") x = torch.randn(1000, 1000, device=cuda_device) y = torch.randn(1000, 1000).to(cuda_device) I made it reasonably performant by having most operations dispatch asynchronously whenever possible. For cases where slow performance is…
Oct 2025 · github.com
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2016 · github.com
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2019 · dev.to
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After the incredible response to our launch of the first online CUDA playground, we have just shipped something we think all you GPU programming and ML enthusiasts will love. Introducing LeetGPU Challenges--the place to compete on writing the fastest CUDA kernels. We have problems like matrix multiplication, agent simulation, multi-head self-attention, with more dropping every couple of days! We have a lot of really cool things coming up, including support for PyTorch, TensorFlow, JAX, TinyGrad; Multi-GPU programs; H100, V100, A100 GPU options Give it a try and let us know what you think!
2025 · leetgpu.com
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May 2026 · twitter.com
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Democratisation of local AI is key. I've been working on pushing the limits of commercial hardware, squeezing any extra bit possible. My Scientific Agentic AI hareness helped me to reallocate every single bit of it. I rewrote the Kernel, I went down the CUDA rabbit hole until I have been able to explain any bit and any ms of computational power involved in the process pushing the Qwen 30B-A3B from 8 tok7s to 19 tok/s with llama.cpp up to 22.2 tok/s with my project and 109 tok/s on not novel content and speeding up the prefill by 5-9X
Jul 2026 · github.com
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I built this mostly because I love the intersection of game AI, high-performance computing, and poker. I’d love for anyone interested in game theory or CUDA optimization to tear it apart, test the accuracy, and give me feedback. Happy to answer any questions about the algorithms, the transition from CPU to GPU, or poker AI in general!
Jul 2026 · bupticybee.github.io
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2020 · github.com
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