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cupy avatar

cupy/cupy

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Cupy

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 integration, allowing array data to be connected to low-level APIs for precise control over hardware execution.

Broadly, the project covers high-performance array processing and scientific computing workflows. Its capabilities include accelerating array computations and providing tools for large-scale numerical calculations.

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Features

  • GPU Acceleration - Provides a comprehensive environment for GPU-accelerated numerical and array computing.
  • GPU Kernel Implementations - Allows integration of custom CUDA kernels for precise low-level hardware control.
  • Numerical Computing Libraries - Acts as a numerical computing library providing high-performance linear algebra and array operations on NVIDIA GPUs.
  • GPU Acceleration Libraries - Serves as a GPU acceleration library for offloading heavy numerical array calculations to graphics hardware.
  • GPU-Accelerated SciPy Implementations - Provides a CUDA-based implementation of SciPy routines to accelerate complex mathematical workloads.
  • Array Processing - Implements high-performance array processing by running NumPy and SciPy style operations on GPUs.
  • High-Performance Scientific Computing - Enables high-performance scientific computing by executing array-based calculations on graphics hardware.
  • Scientific Computing - Supports scientific computing workflows by leveraging GPU acceleration for massive datasets.
  • Vectorized Array Operations - Accelerates large-scale array computations using GPU-powered vectorized operations.
  • Numerical Computing - Provides GPU-accelerated numerical computing capabilities for scientific and engineering workloads.
  • NumPy Array Integration - Implements a NumPy-compatible array interface to allow seamless transition of computations from CPU to GPU.
  • Kernel Dispatchers - Provides a kernel dispatcher that selects the optimal GPU implementation based on data types and dimensions.
  • Shared Memory Data Exchange - Enables high-performance tensor data exchange between deep learning frameworks using zero-copy memory buffers.
  • Shared Memory Transports - Utilizes memory-mapped buffer sharing to enable zero-copy data exchange between different libraries.
  • GPU Memory Allocators - Implements specialized GPU memory allocators and pooling to minimize allocation overhead.
  • Python-C Interfaces - Provides high-performance Python-C++ interfaces to wrap low-level CUDA calls for scientific computing.
  • JIT Kernel Compilers - Generates optimized GPU machine code at runtime via a just-in-time compiler for array operations.
  • Parallel Processing - CUDA-accelerated library with a NumPy-compatible API.
  • Big Data and Distributed Computing - CUDA-accelerated NumPy-compatible array operations.
  • Data Manipulation Libraries - NVIDIA CUDA-accelerated NumPy-compatible array library.
  • Computation and Optimization - NumPy-compatible multi-dimensional array implementation for CUDA.
  • Core Frameworks and Libraries - NumPy and SciPy-compatible array library for GPU acceleration.
  • Numerical Computation - Provides GPU-accelerated array operations with a NumPy-compatible API.
  • Parallel and High-Performance Computing - CUDA-accelerated library with a NumPy-like API.
11,000 stars·1,040 forks·Python·MIT·66 views

Star history

Star history chart for cupy/cupyStar history chart for cupy/cupy

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does cupy/cupy do?

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.

What are the main features of cupy/cupy?

The main features of cupy/cupy are: GPU Acceleration, GPU Kernel Implementations, Numerical Computing Libraries, GPU Acceleration Libraries, GPU-Accelerated SciPy Implementations, Array Processing, High-Performance Scientific Computing, Scientific Computing.

Which projects share features with cupy/cupy?

Projects with overlapping indexed features include: 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… numpy/numpy — NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing… scipy/scipy — SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms… arrayfire/arrayfire — ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance… triton-lang/triton — Triton is a parallel computing framework and high-level programming language designed for writing custom compute…