NVIDIA's official CUDA Samples repository is a large catalog of self-contained, compilable example programs demonstrating the CUDA platform across C++ and Python. The C++ collection spans hundreds of samples organized into ten categories: fundamentals (vector add, matrix multiply), utilities (deviceQuery), concepts and techniques (reduction, scan, convolution, histograms), feature showcases (CUDA graphs, cooperative groups, Tensor Core GEMMs, dynamic parallelism, NVRTC JIT), CUDA libraries (cuFFT, cuRAND, cuBLAS, cuSOLVER, CUB), domain-specific applications (Black-Scholes, N-body, optical flow, fluid simulation, volume rendering, Monte Carlo), performance studies, libNVVM/PTX generation, platform-specific Tegra/embedded examples, and CUDA Tile C++ microbenchmarks. A parallel Python collection uses the modern cuda.core API with CuPy/NumPy interop and framework integrations (PyTorch, TensorFlow, RAPIDS). Each sample is typically a standalone CLI or interactive graphics program with its own main() entry point; most include a CPU 'gold' reference implementation and automated pass/fail verification, making the collection both educational and regression-tested.
Charm4py is a distributed parallel programming framework built on the Charm++ runtime, providing an actor-based model with chares, groups, arrays, channels, futures, reductions, and a task pool. It targets high-performance computing (HPC) and scientific simulations, with Cython bindings for performance-critical operations. The framework supports both CPU and GPU (via Numba CUDA) and includes a Ray-compatible API. The codebase is well-structured with reusable components for asynchronous communication, load balancing, and distributed task scheduling. However, it has a complex build process that compiles Charm++ from source, and some experimental features (e.g., pool, object store) may be incomplete. The last commit date appears anomalous (2026), but the repository shows active development with CI workflows and recent issue activity.