#High-performance-computing
Showing 44 of 44 repositories tagged #high-performance-computing, ranked by stars
Build, Manage and Deploy AI/ML Systems
High-performance TensorFlow library for quantitative finance.
Training and serving large-scale neural networks with auto parallelization.
cuda-oxide is an experimental Rust-to-CUDA compiler that lets you write (SIMT) GPU kernels in safe(ish), idiomatic Rust. It compiles standard Rust code directly to PTX — no DSLs, no foreign language bindings, just Rust.
A list of awesome compiler projects and papers for tensor computation and deep learning.
Open-source software for volunteer computing and grid computing.
Hermit for Rust.
Acceleration package for neural networks on multi-core CPUs
A Rust-based, lightweight unikernel.
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
An R-focused pipeline toolkit for reproducibility and high-performance computing
Function-oriented Make-like declarative workflows for R
Linear algebra in TypeScript.
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
Graphics Processing Units Molecular Dynamics
A zero-dependency ML framework in C with a modern Python API for full control over execution and memory.
FlashAttention (Metal Port)
Image-processing software for cryo-electron microscopy
Run Slurm in Kubernetes
Run Slurm on Kubernetes. A Slinky project.
A personal research and development (R&D) lab that facilitates the sharing of knowledge.
A Clojure dataframe library that runs on Spark
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
Summaries / Cheat Sheets created at ETH Zurich BsC Computer Science & MsC Data Science
High performance data processing employs high performance computing (HPC) to process data, which is then translated into information and knowledge. The advent of high-performance computing and data analytics enabled real-time interrogation of extremely large data sets.
Archetypes for targets and pipelines
The Accelerator is a tool for fast and reproducible processing of large amounts of data.
A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.
TePDist (TEnsor Program DISTributed) is an HLO-level automatic distributed system for DL models.
:snake: Snakefiles for common RNA-seq data analysis workflows (STAR and Kallisto).
Open source digital rocks software platform for micro-CT, CT, thin sections and borehole image analysis. Includes tools for: annotation, AI, HPC, porous media flow simulation, porosity analysis, permeability analysis and much more.
A Data-Centric Compiler for Machine Learning
Quickly generate, start and analyze benchmarks for molecular dynamics simulations.
Automated Parallelization System and Infrastructure for Multiple Ecosystems
Radio-Frequency Engineering Modeling Toolkit (RF-EMT)
A minimal example data analysis project with the targets R package
Example workflows for the drake R package
The user manual for the drake R package
User manual of the targets R package
A Next-Gen AI-native Kernel programming DSL for Maximizing Productivity
Open source skill library for AI coding agents to write, optimize, and debug high performance compute kernels across CUDA, Triton, and quantized workloads.
Aeron proxy generator
ATLAS is a sophisticated real-time risk analysis system designed for institutional-grade market risk assessment. Built with high-frequency trading (HFT) capabilities and advanced machine learning techniques, ATLAS provides continuous volatility predictions and risk metrics using both historical patterns and real-time market data.
High Performance Quantative Finance on JAX