Rust SIMD on the GPU
VectorWare has successfully implemented Rust's portable SIMD on GPU hardware, allowing developers to use high-level Rust abstractions for parallel computing. This development bridges the gap between CPU-based vectorization and GPU-native performance.
Why it matters
It simplifies GPU programming by allowing developers to write cross-platform, high-performance code without relying on architecture-specific vendor intrinsics.
GPU code can now use Rust's portable SIMD. We share the implementation approach and what this unlocks for GPU programming.
At VectorWare , we are building the first GPU-native software company . Today, we are excited to announce that we can successfully use Rust's portable SIMD ( core::simd ) on the GPU. This milestone marks a significant step towards our vision of enabling developers to write complex, high-performance applications that leverage the full power of GPU hardware using familiar Rust abstractions.
When we brought Rust threads to the GPU , we mapped each std::thread to a GPU warp . This let us run many concurrent threads on the GPU but did not use the parallel lanes within each thread/warp.
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