Awesome GitHub RepositoriesGPU Stream Scheduling

cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large tabular datasets. It provides a high-level API for executing filtering, joining, and aggregating operations directly on GPU hardware. The project integrates the Apache Arrow memory format to enable zero-copy data transfers and includes a just-in-time compiler for executing custom user-defined functions on the GPU. The library features specialized acceleration for existing workflows by redirecting standard Pandas dataframe calls and Polars query plans to a GPU backend. It also p

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

CUDA.jl provides a programming interface for executing custom kernels and performing parallel array computing directly on NVIDIA graphics hardware using the Julia language. It serves as a framework for compiling and scheduling user-defined functions across multiple processing cores, enabling high-performance data processing and task synchronization. The library distinguishes itself through a custom compiler backend that translates high-level language functions into hardware-specific machine code. It manages complex hardware interactions through asynchronous stream scheduling, unified memory m