High-Performance Computing
High-performance computing (HPC) uses powerful processors, accelerators, and parallel software to solve computationally demanding problems. By dividing workloads across many cores or machines, HPC can shorten the time needed for tasks such as scientific simulation, large-scale data analysis, and training complex models. It also addresses challenges in memory use, communication between processors, and efficient scheduling of work across available hardware.
Open source HPC tools include parallel programming libraries, GPU kernels, workload schedulers, performance profilers, and optimized numerical software. When choosing tools, consider hardware compatibility, scalability, documentation, license terms, maintenance activity, and how easily they fit existing workflows. HPC is useful to researchers, engineers, developers, and organizations that need to process large workloads efficiently, whether on a local accelerator, a cluster, or a cloud computing environment.
2 repositories · updated September 26, 2026
KDA: Kernel Design Agents for High-Performance CUDA Kernel Development
Kernel Design Agents (KDA) offers an agent-centric workflow designed to streamline the research, implementation, verification, and iteration of performance-sensitive CUDA kernel tasks. This innovative approach leverages coding agents to accelerate the development of high-performance kernels. It is an early research prototype from NVlabs, welcoming community feedback and contributions.
ZLUDA: Run CUDA Applications on Non-NVIDIA GPUs with Near-Native Performance
ZLUDA is an innovative open-source project providing a drop-in replacement for CUDA, enabling users to run CUDA applications on non-NVIDIA GPUs. Written in Rust, it aims to deliver near-native performance, significantly expanding hardware compatibility for CUDA-dependent software. This project offers a powerful solution for greater flexibility in GPU computing environments.