NVIDIA introduces CUDA Rust with two tracks for writing GPU kernels
A new paper presents a portable, safe, and fast approach to GPU offloading in Rust, aiming to make GPU programming more accessible and reliable. The work addresses key challenges in GPU development by leveraging Rust's safety guarantees while maintaining performance.
A new paper presents a portable, safe, and fast approach to GPU offloading in Rust, aiming to make GPU programming more accessible and reliable. The work addresses key challenges in GPU development by leveraging Rust's safety guarantees while maintaining performance.
CUDA for AMD on Windows project surfaces on GitHub via Hacker News
CUDA for AMD on Windows project surfaces on GitHub via Hacker News
PyTorch Monarch, a library for efficient large-scale model training, has been ported to run on AMD GPUs. This enables AMD GPU users to leverage Monarch's structured matrix operations for faster training of transformers and other architectures.
PyTorch Monarch, a library for efficient large-scale model training, has been ported to run on AMD GPUs. This enables AMD GPU users to leverage Monarch's structured matrix operations for faster training of transformers and other architectures.
AMD released a machine-readable instruction set architecture (ISA) specification, enabling frontier AI models to automatically generate optimized GPU kernels for AMD hardware. This move aims to simplify kernel development and improve performance for AI workloads on AMD GPUs.
AMD released a machine-readable instruction set architecture (ISA) specification, enabling frontier AI models to automatically generate optimized GPU kernels for AMD hardware. This move aims to simplify kernel development and improve performance for AI workloads on AMD GPUs.