Awesome GitHub RepositoriesTiled Memory Access Patterns

Triton is a parallel computing framework and high-level programming language designed for writing custom compute kernels. It functions as a deep learning compiler, translating complex mathematical operations into high-throughput instructions that maximize hardware utilization and memory efficiency on graphics processing units. The framework distinguishes itself through a hardware-agnostic compute abstraction that allows developers to define kernels without manual low-level tuning. It employs just-in-time compilation to generate optimized binary instructions at runtime, utilizing static data f

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

This project is a comprehensive educational resource and programming course covering C++ language semantics and features from C++03 through C++26. It provides structured tutorials and technical guides focused on modern C++ development. The material offers specialized instruction on template metaprogramming, including the use of type traits and compile-time computations. It features detailed guides on concurrency and parallelism for multi-core execution, as well as a reference for software design applying SOLID principles and RAII. Additionally, it covers build performance optimization to redu

Cutlass is a collection of C++ templates and Python interfaces for implementing high-performance linear algebra operations on NVIDIA GPUs. It provides a kernel composition framework for designing custom GPU kernels and a mixed-precision tensor library capable of executing operations across diverse data formats, ranging from 64-bit floating point to 4-bit integers. The project features a toolkit for operator fusion that integrates activation functions and bias calculations directly into matrix multiplication kernels to reduce memory passes. It also includes a Python-based domain-specific langu

LeetCUDA is a collection of high-performance GPU kernel libraries focusing on memory optimization, activation functions, and attention mechanisms. It serves as a reference library for CUDA kernel implementations, ranging from basic element-wise operations to complex neural network components, and provides Python bindings to integrate these kernels into deep learning workflows. The project is distinguished by its focus on low-level hardware optimizations. This includes the use of tensor cores for half-precision matrix multiplication, asynchronous data pipelining with double buffering, and shar

XiangShan is a high-performance RISC-V processor core and a hardware description language framework. It provides a construction-based system for designing, simulating, and verifying complex processor micro-architectures and peripheral devices. The project includes a high-performance CPU simulator used for architectural exploration and functional verification of processor execution. The project implements a superscalar out-of-order CPU architecture that uses renaming and reorder buffers to execute instructions in parallel. It generates synthesizable Verilog files from hardware descriptions to

Flashlight هي مكتبة تعلم آلي مستقلة بلغة C++ ومكتبة موترات تستخدم لبناء وتدريب الشبكات العصبية. تعمل كإطار عمل شامل للشبكات العصبية ومحرك للتمايز التلقائي، مما يوفر الأدوات لبناء رسوم بيانية للحساب وحساب التدرجات عبر الانتشار العكسي. يعمل المشروع كإطار عمل للتدريب الموزع، حيث يستخدم عمليات (All-reduce) لمزامنة التدرجات والمعلمات عبر عقد حساب وأجهزة متعددة. يتميز بالتكامل العميق لمعالجة الموترات عالية الأداء، وقابلية التشغيل البيني لذاكرة الجهاز الأصلية، ونظام لمزامنة الأوزان عبر العمال الموزعين لتسريع تدريب النماذج واسعة النطاق. يغطي إطار العمل مجموعة واسعة من قدرات التعلم العميق، بما في ذلك تكوين الطبقات المعيارية لتصميم بنيات معقدة مثل الكتل المتبقية (Residual blocks) والخلايا المتكررة. يوفر أدوات واسعة النطاق لإدارة البيانات للاستيعاب والجلب المسبق، إلى جانب أنظمة التسلسل لحفظ حالات النموذج. بالإضافة إلى ذلك، يتضمن مجموعة من أدوات المراقبة وقابلية المراقبة لتتبع مقاييس التدريب وقياس أخطاء التسلسل. تم تنفيذ المكتبة بلغة C++.

TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc

perf-ninja is a collection of educational resources and curricula focused on CPU architecture, memory hierarchies, SIMD programming, and low-level performance engineering. It provides instructional material and practical labs for identifying and fixing CPU bottlenecks, such as cache misses and branch mispredictions. The project differentiates itself through specialized training in hardware-level optimizations, including the use of compiler intrinsics for SIMD vectorization and the implementation of branchless predicate execution to eliminate pipeline stalls. It also covers advanced binary-lev