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.

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.

Data Manipulation Libraries - NVIDIA CUDA-accelerated NumPy-compatible array library.

Computation and Optimization - NumPy-compatible multi-dimensional array implementation for CUDA.

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.

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Features

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