Awesome GitHub RepositoriesTensor Cloning

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

Torch7 is a scientific computing environment and tensor computation library used for deep learning research and numerical analysis. It functions as a Lua-based framework for training neural networks and learning agents, providing a toolkit for implementing architectures and training through reinforcement learning algorithms. The project is distinguished by its tight integration with C, utilizing a binding layer to map high-level scripting to low-level C structures for direct memory access. It supports hardware-accelerated computation by offloading linear algebra and convolution operations to

ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,

gpu.cpp ist eine leichtgewichtige C++-Bibliothek zur Ausführung von Low-Level-General-Purpose-GPU-Berechnungen über verschiedene Hardwareanbieter und Betriebssysteme hinweg. Sie fungiert als portabler GPU-Wrapper, Kernel-Orchestrator und Tensor-Managementsystem unter Verwendung der WebGPU-Spezifikation, um Geräteinitialisierung, Pufferübertragungen und Compute-Shader-Dispatching zu abstrahieren. Die Bibliothek bietet ein Framework zur Definition von Compute-Kerneln aus Shader-Code und zur Verwaltung ihrer asynchronen Ausführung und Synchronisation. Sie ermöglicht die Ausführung plattformübergreifender Compute-Shader und die Orchestrierung von GPU-Aufgaben durch eine standardisierte Grafikprozessor-Spezifikation. Das System handhabt den gesamten Lebenszyklus von GPU-Speicher, einschließlich der Allokation mehrdimensionaler Tensoren, bidirektionaler Datenbewegung zwischen Host und Gerät über Staging-Puffer sowie Ressourcen-Tracking zur Vermeidung von Speicherlecks. Es unterstützt zudem Tensor-Slicing zur Erstellung nicht-besitzender Views von Speichersegmenten und enthält Dienstprogramme für System-Message-Logging und Schweregrad-Filterung.