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Julia is a high-level, high-performance dynamic language for technical computing. org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below.

This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models and their inference.

LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. py. You'll recognize this file as a slightly tweaked nanoGPT, an earlier project of mine.

DeepEP (DeepEveryParallel) is a high-performance communication library for modern machine learning training and inference.

Build your AI agents, chatbots, and RAG applications with HPC-AI Model APIs!

Open-source explicit finite element solver for simulating crashes, impacts, explosions, and other dynamic events.

Taskflow helps you quickly write high-performance task-parallel programs with high programming productivity. It is faster, more expressive, fewer lines of code, and easier for drop-in integration than many existing task programming libraries.

TIP] > This is a source-grounded course for engineers who need to make architecture decisions under real constraints. Complete the engineering foundation first if you cannot yet trace, debug, disassemble, structure, and review production-shaped services.

A high-performance communication library for machine learning training and inference on Huawei Ascend NPUs.