Awesome GitHub RepositoriesGPU Kernel Programming

AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

GPU-Puzzles is an interactive learning environment and tutorial designed for mastering CUDA GPU kernel development. It serves as an educational tool and lab where users solve coding puzzles to understand how to map high-level logic to low-level GPU hardware instructions. The platform focuses on teaching parallel computing concepts and GPU architecture. Users practice developing parallel algorithms and managing GPU memory through a series of hands-on challenges. The environment utilizes a bridge between Python and CUDA to execute kernels and provide real-time feedback by validating outputs ag

This repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves as a GPGPU implementation guide and a parallel computing reference, providing code for using graphics hardware to perform general-purpose calculations and high-performance parallel processing. The project provides specific samples for GPU kernel development and resource management. These include demonstrations of multi-GPU communication, peer-to-peer memory access, and system hardware inspection to coordinate distributed GPU resources. The codebase covers a wide range of capa

This project is a collection of reference implementations and benchmarks demonstrating the use of the Vulkan graphics and compute API. It provides a set of cross-platform examples and GPU programming patterns designed for high-performance rendering and hardware-accelerated tasks. The repository includes a suite of performance benchmarks used to measure API behavior across different hardware environments. It features a modular architecture that organizes rendering examples into isolated units, along with command-line utilities for the batch execution of sample sequences. The project covers se

rust-cuda is a GPU programming framework and device compiler that allows for the development and execution of high-performance kernels on NVIDIA hardware using Rust. It provides a driver wrapper to manage device memory allocation and kernel launching, effectively serving as a system for writing GPU compute logic without relying on C++. The project includes a compute library with hardware-optimized primitives for neural network acceleration and hardware-accelerated raytracing. It utilizes a compilation toolchain that translates source code into a low-level intermediate representation for execu

This project is a comprehensive educational resource and curriculum focused on the design and implementation of the full machine learning software and hardware stack. It serves as a technical reference for architecting machine learning systems, spanning from low-level programming interfaces to large-scale deployment infrastructure. The project provides instructional guidance on several specialized domains, including the development of AI compilers through intermediate representations and graph optimizations. It covers the architectural patterns required for distributed training across GPU clu

HIP is a C++ GPU kernel language and cross-platform runtime designed for writing portable high-performance compute applications. It provides a programming interface that allows a single source codebase to execute on both AMD and NVIDIA GPU architectures. The project functions as a compatibility layer that enables the conversion and migration of existing CUDA source code to run on AMD hardware. This is achieved through a syntax mapping that mirrors CUDA and a source-to-source translation process during compilation. The toolkit covers the broader surface of cross-platform GPGPU development, in

warp-ctc, सीक्वेंस-टू-सीक्वेंस डीप लर्निंग मॉडल्स को ट्रेन करने के लिए कनेक्शनिस्ट टेम्पोरल क्लासिफिकेशन (CTC) लॉस की गणना करने के लिए एक उच्च-प्रदर्शन लाइब्रेरी है। यह लंबी सीक्वेंस के लिए संभावना गणना के दौरान अंडरफ्लो और प्रिसिजन एरर को रोकने के लिए लॉग-स्पेस कंप्यूटेशन का उपयोग करके एक न्यूमेरिकल स्टेबिलिटी लेयर प्रदान करती है। यह लाइब्रेरी CPU और GPU आर्किटेक्चर में समानांतर रूप से लॉस की गणना करने के लिए हार्डवेयर-एक्सेलेरेटेड कर्नेल का उपयोग करती है। यह CTC एल्गोरिदम के डायनामिक प्रोग्रामिंग स्टेप्स को ऑप्टिमाइज़ करके ट्रेनिंग थ्रूपुट बढ़ाने पर केंद्रित है। ये क्षमताएं स्पीच रिकग्निशन, हैंडराइटिंग ऑप्टिकल कैरेक्टर रिकग्निशन, और सामान्य सीक्वेंस-टू-सीक्वेंस मैपिंग के लिए मॉडल्स की ट्रेनिंग का समर्थन करती हैं। इस प्रोजेक्ट में TensorFlow के भीतर लॉस की गणना और एलाइनमेंट-फ्री ट्रेनिंग के लिए एकीकरण शामिल है।

gpu.cpp is a lightweight C++ library for executing low-level general-purpose GPU computation across different hardware vendors and operating systems. It functions as a portable GPU wrapper, kernel orchestrator, and tensor management system using the WebGPU specification to abstract device initialization, buffer transfers, and compute shader dispatching. The library provides a framework for defining compute kernels from shader code and managing their asynchronous dispatch and synchronization. It enables the execution of cross-platform compute shaders and the orchestration of GPU tasks through

Zen-C is a multi-target systems language and source-to-source compiler that translates high-level logic into human-readable GNU C or C11 code. It functions as a JIT-enabled programming language with an in-process compiler for real-time interactive code evaluation and testing. The project serves as a CUDA GPU kernel generator, mapping specialized syntax to CUDA C++ using device attributes to target graphics hardware. It acts as an interoperability layer capable of emitting compatible code for C++, Objective-C, and Lisp to bridge native system frameworks and libraries. The language includes an

cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic wrapper for controlling device memory, managing hardware toolchains, and orchestrating execution graphs on NVIDIA GPUs, allowing for the compilation and launching of parallel kernels directly from Python. The project enables the development of SIMT kernels and the execution of mathematical algorithms on device memory. It integrates pre-compiled bytecode as custom operators and interfaces with accelerated device libraries to access low-level hardware functions without leaving the la