this might be an extremely stupid question, but is this just a demo project of https://github.com/ffmpegwasm/ffmpeg.wasm? or is this bringing forth some other utility that im not seeing?
Hey, I'm Arpad. I have a background in signal processing, deep learning (mainly computer vision), and embedded systems. I have 5+ years of research experience and end-to-end edge AI development. My strong suite comes in GPU + embedded development, optimization, and architecture design.
Best fit: fast-moving teams, end-to-end ownership, collaborative.
looks like a cool project, but id say keep working on it since there seems to be some confusion on why someone would want to use this: no benchmarks and overall pretty vibe-codey (which id personally be very hesitant to use in production)
another comment already mentioned comparison to vortex, which is the same compression ratio and same speeds as youre claiming - but your compression is half of parquet. and if speed is the main goal youre going for, python is an interesting choice. no hate, but def keep working on it, and would love to see more concrete benchmarks with various columnar store types
1. use-after-free, drop semantics vs manual cudaFree
2. kernel args enforced using `cuda_launch!` whereas CPP void* args is just an array of pointers, validating count only
3. alias mutable writes. e.g. CPP can have more than one thread writing out[i] with same i and this will compile. but DisjointSlice<T> with ThreadIndex doesnt have any public constructor (see: https://github.com/NVlabs/cuda-oxide/blob/2a03dfd9d5f3ecba52...) and only using API of `index_1d` `index_2d` and `index_2d_runtime`
perhaps not drop-in, but all my workflows with cudarc have always been "i make cuda kernel, i use cudarc for ffi to said kernels, i call via rust" - which for this case is pretty analogous
briefly looking at the repo, looks like the main workflow is using rustc-codegen-cuda to convert rust -> MIR -> pliron IR -> LLVM IR -> PTX, which is embedded in the host binary, where then cuda-core loads embedded PTX at runtime onto the GPU
but, if you arent directly making cuda kernels and just want cudarc for either calling existing kernels or other cuda driver api access then cudarc is lighter-weight option? or just use one of the sub-crates in this repo like cuda-core for those apis
This is amazing.. ive been working with custom CUDA kernels and https://crates.io/crates/cudarc for a long time, and this honestly looks like it could be a near drop-in replacement.
im especially curious how build times would compare? Most Rust CUDA crates obv rely on calling CMake or nvcc, which can make compilation painfully slow. coincidentally, just last week i was profiling build times and found that tools like sccache can dramatically reduce rebuild times by caching artifacts - but you still end up paying for expensive custom nvcc invocations (e.g. candle by hugging face calls custom nvcc command in their kernel compilation): https://arpadvoros.com/posts/2026/05/05/speeding-up-rust-whi...
i've had my shot at sycamore a number of times. IMO leptos (leptos.dev) has far more fine-grained capabilities, and dioxus (dioxuslabs.com) is overall more hand-holdy but also powerful. comes with tradeoff for speed. wasm still isnt there yet (yet..) but a lot more web frameworks (including smaller rust ones) can be tracked here: https://krausest.github.io/js-framework-benchmark/current.ht...