Awesome GitHub RepositoriesShared Memory Management

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

MAME is a vintage hardware emulation platform designed to recreate the circuitry of arcade games, computers, and consoles to run original software on modern devices. It functions as a retro gaming preservation framework for managing, verifying, and archiving ROM sets and disk images to ensure long-term software accessibility. The project features a system debugging tool for inspecting emulated memory, CPU registers, and execution flow via breakpoints and disassembly. It also includes a Lua-based automation layer that exposes core system state and hardware controls for custom behavior and anal

This is an open-source, crowd-sourced wiki textbook that teaches Linux system programming in C. It covers the core operating system concepts of process management through the fork-exec-wait model, dynamic memory allocation using implicit free list heap allocators, inode-based file systems, inter-process communication via pipes and shared memory, POSIX threads with synchronization primitives, signal-based asynchronous notification, virtual memory with page table translation, and runtime diagnostics using Valgrind and GDB. The textbook distinguishes itself by providing practical, implementation

This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

Dieses Projekt ist eine Bildungsressource, die ein umfassendes Entwicklungs-Tutorial zum Schreiben und Laden von eBPF-Programmen unter Verwendung von C, Go und Rust innerhalb des Linux-Kernels bietet. Es dient als technischer Leitfaden für die Entwicklung benutzerdefinierter Logik zur direkten Ausführung im Kernel. Die Materialien decken spezialisierte Bereiche ab, einschließlich Kernel-Observability und Tracing, Sicherheitsimplementierung für Intrusion-Detection und High-Performance-Network-Engineering für Paketfilterung und Load Balancing. Es enthält zudem dedizierte Handbücher für Linux-Kernel-Tracing und die Verwendung von kprobes, uprobes und tracepoints. Das Projekt umfasst ein breites Spektrum an Funktionsbereichen, wie Kernel-Instrumentierung, Systemüberwachung und Observability, Netzwerkanalyse und Sicherheitsdurchsetzung. Es erstreckt sich zudem auf Hardware-Level-Debugging für GPUs und Treiber sowie auf Low-Level-Systemmanipulation und Ressourcenmanagement.