Awesome GitHub RepositoriesGPU Kernel Assertions

XXPermissions is a framework and manager for handling runtime and special system permissions across different Android operating system versions. It provides a unified interface for requesting standard permissions and directing users to system settings pages for advanced access control, such as file access and accessibility services. The project features a backward compatibility layer that abstracts version-specific logic, removing the need for manual operating system version checks in client code. It utilizes a chainable request pipeline to queue multiple permissions and manage their asynchro

The Rust RFCs repository is the formal home for the Rust language evolution process, housing the structured design documents and community review mechanisms that govern changes to the Rust programming language, its compiler, and its standard library. It defines the complete lifecycle for proposing, discussing, and implementing substantial changes through RFC documents, from initial submission and community feedback through final comment periods and sub-team sign-offs. The repository codifies the governance and collaboration processes that shape Rust's development, including mechanisms for com

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