Awesome GitHub RepositoriesMemory Access Pattern Optimizers

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

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 este o bibliotecă C++ standalone de machine learning și tensori, utilizată pentru construirea și antrenarea rețelelor neuronale. Aceasta funcționează ca un framework cuprinzător de rețele neuronale și motor de diferențiere automată, oferind instrumentele necesare pentru a construi grafuri de calcul și a calcula gradienții prin backpropagation. Proiectul servește drept framework de antrenare distribuită, utilizând operațiuni all-reduce pentru a sincroniza gradienții și parametrii pe mai multe noduri de calcul și dispozitive. Se distinge prin integrarea profundă a manipulării de înaltă performanță a tensorilor, interoperabilitatea nativă a memoriei dispozitivului și un sistem pentru sincronizarea ponderilor între workerii distribuiți pentru a accelera antrenarea modelelor la scară largă. Framework-ul acoperă o gamă largă de capabilități de deep learning, inclusiv compoziția modulară a straturilor pentru proiectarea arhitecturilor complexe precum blocuri reziduale și celule recurente. Oferă utilitare extinse de gestionare a datelor pentru ingestie și prefetching, alături de sisteme de serializare pentru persistența stărilor modelelor. În plus, include o suită de instrumente de monitorizare și observabilitate pentru urmărirea metricilor de antrenare și măsurarea erorilor de secvență. Biblioteca este implementată în 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