Awesome GitHub RepositoriesKernel Debuggers

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

This project is a comprehensive educational framework designed to guide learners through the complexities of systems engineering and low-level software development. It provides structured learning paths that integrate hardware simulation, source code analysis, and project-based exercises to help developers master the foundational concepts of computer architecture, operating systems, and firmware design. The curriculum distinguishes itself by emphasizing direct interaction with system internals, requiring learners to examine and modify existing open-source kernel and driver implementations. By

SSHFS-Win is a Windows implementation of SSHFS that mounts remote directories over SSH as local Windows drives, enabling seamless file access as if they were local network drives. It provides both command-line and graphical interfaces for creating, managing, and disconnecting SSHFS mounts, supporting password or SSH key authentication with optional credential storage in the Windows Credential Manager. The project extends beyond basic SSH mounting to support a wide range of remote file access scenarios, including mounting cloud storage services like Azure Blob or Amazon S3, distributed POSIX f

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

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

HyperDbg is a hardware-assisted kernel-mode debugging platform that leverages virtualization to monitor and control system execution. By utilizing hypervisor-level primitives, it enables deep system analysis and instrumentation without relying on standard operating system debugging interfaces. The framework provides a comprehensive environment for inspecting both kernel and user-mode processes, allowing for granular control over execution flow and system state. The project distinguishes itself through a transparent debugging layer designed to remain invisible to the target environment. It emp