Home · CUDA.jl - JuliaGPU
The CUDA.jl package is the main entrypoint for programming NVIDIA GPUs in Julia. The package makes it possible to do so at various abstraction levels, from easy-to-use arrays down to hand-written kernels using low-level CUDA APIs.
JuliaGPU
The best supported GPU platform in Julia is NVIDIA CUDA, with mature and full-featured packages for both low-level kernel programming as well as working with high-level operations on arrays. All versions of Julia are supported, on Linux and Windows, and the functionality is actively used by a variety of applications and libraries.
JuliaGPU/CUDA.jl: CUDA programming in Julia. - GitHub
The CUDA.jl package is the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
Introduction · CUDA.jl - JuliaGPU
A gentle introduction to parallelization and GPU programming in Julia. Julia has first-class support for GPU programming: you can use high-level abstractions or obtain fine-grained control, all without ever leaving your favorite programming language.
Array programming · CUDA.jl - JuliaGPU
The easiest way to use the GPU's massive parallelism, is by expressing operations in terms of arrays: CUDA.jl provides an array type, CuArray, and many specialized array operations that execute efficiently on the GPU hardware. In this section, we …
NVIDIA CUDA ⋅ JuliaGPU
CUDA.jl makes it possible to program NVIDIA GPUs at different abstraction levels: by using the CuArray type, providing a user-friendly yet powerful abstraction that does not require any GPU programming experience; by writing CUDA kernels, with the …
High-Performance GPU Computing in the Julia Programming …
The CUDAnative.jl package adds native GPU programming capabilities to the Julia programming language. Used together with the CUDAdrv.jl or CUDArt.jl package for interfacing with the CUDA driver and runtime libraries, respectively, you can now do low-level CUDA development in Julia without an external language or compiler. As an introductory ...
CUDA.jl 3.0 - JuliaGPU
2021年4月9日 · CUDA.jl 3.0 is a significant, semi-breaking release that features greatly improved multi-tasking and multi-threading, support for CUDA 11.2 and its new memory allocator, compiler tooling for GPU method overrides, device-side random number generation and a completely revamped cuDNN interface.
JuliaGPU - GitHub
CUDA programming in Julia. Reusable compiler infrastructure for Julia GPU backends. Julia support for the oneAPI programming toolkit. Reusable array functionality for Julia's various GPU backends.
Overview · CUDA.jl - JuliaGPU
Overview. The Julia CUDA stack only requires users to have a functional NVIDIA driver. It is not necessary to install the CUDA toolkit. On Windows, also make sure you have the Visual C++ redistributable installed.
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