There are 96 repositories under hpc topic.
Making large AI models cheaper, faster and more accessible
A Cloud Native Batch System (Project under CNCF)
A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.
A DSL for data-driven computational pipelines
Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild
Singularity has been renamed to Apptainer as part of us moving the project to the Linux Foundation. This repo has been persisted as a snapshot right before the changes.
a Productive Parallel Programming Language
MooseFS Distributed Storage – Open Source, Petabyte, Fault-Tolerant, Highly Performing, Scalable Network Distributed File System / Software-Defined Storage
Playing around "Less Slow" coding practices in C++ 20, C, CUDA, PTX, & Assembly, from numerics & SIMD to coroutines, ranges, exception handling, networking and user-space IO
Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
A basic user tool to execute simple docker containers in batch or interactive systems without root privileges.
Unified Communication X (mailing list - https://elist.ornl.gov/mailman/listinfo/ucx-group)
This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several basic kernel optimizations, including: elementwise, reduce, sgemv, sgemm, etc. The performance of these kernels is basically at or near the theoretical limit.
A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC
A curated list of awesome high performance computing resources
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
:rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone