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[ARCHIVED] The C++ Standard Library for your entire system. See https://github.com/NVIDIA/cccl
The main features of nvidia/libcudacxx are: Parallel Processing, Concurrency and Parallelism, Parallel and High-Performance Computing.
Projects with overlapping indexed features include: arrayfire/arrayfire — ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance… ddemidov/vexcl — VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP. numba/numba — Numba is a just-in-time compiler that translates high-level Python functions into optimized machine code at runtime.… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… cupy/cupy — CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and… nvidia/tensorrt — TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural…
VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance numerical computing. It serves as a GPU numerical computing library and parallel signal processing toolkit that abstracts hardware backends, allowing the same codebase to execute across various GPU architectures and CPUs. The project distinguishes itself through a JIT engine that uses expression compilation to fuse operations and minimize memory overhead. It employs a deferred execution graph to optimize computation chains and provides interoperability primitives to share data and e
Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio