What does rust-gpu/rust-cuda do?

rust-cuda is a GPU programming framework and device compiler that allows for the development and execution of high-performance kernels on NVIDIA hardware using Rust. It provides a driver wrapper to manage device memory allocation and kernel launching, effectively serving as a system for writing GPU compute logic without relying on C++.

What are the main features of rust-gpu/rust-cuda?

The main features of rust-gpu/rust-cuda are: GPU Compilers, GPU Kernel Programming, Compute Libraries, GPU Resource Management, CUDA Driver Wrappers, GPU Kernel Development, CUDA Kernel Compilers, GPU Compute Toolchains.

What are some open-source alternatives to rust-gpu/rust-cuda?

Open-source alternatives to rust-gpu/rust-cuda include: nvidia/cuda-python — cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic… z-libs/zen-c — Zen-C is a multi-target systems language and source-to-source compiler that translates high-level logic into… answerdotai/gpu.cpp — gpu.cpp is a lightweight C++ library for executing low-level general-purpose GPU computation across different hardware… nvidia/cuda-samples — This repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves… microsoft/directx-graphics-samples — This project is a collection of reference implementations and technical guides for building high-performance 3D… vladmandic/sdnext — SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing…

Rust Cuda - write high-performance NVIDIA GPU…

Rust Cuda - write high-performance NVIDIA GPU… | Awesome Repos

Features

GPU Compilers - Provides a complete toolchain that translates Rust source code into optimized NVIDIA GPU hardware instructions.
GPU Kernel Programming - Provides a framework for writing and executing high-performance GPU kernels using Rust.
Compute Libraries - Ships a compute library with hardware-optimized primitives for neural networks and raytracing.
GPU Resource Management - Implements a high-level driver interface to manage the lifecycle and allocation of GPU buffers and kernels.
CUDA Driver Wrappers - Provides a programmatic abstraction for controlling NVIDIA GPU device memory and kernel execution.
GPU Kernel Development - Provides a framework for managing thread indices and memory allocation to create device-side logic.
CUDA Kernel Compilers - Compiles GPU source code into target-specific CUDA kernels with a Rust interface.
GPU Compute Toolchains - Provides an integrated development environment and compiler for writing high-performance Rust compute kernels.
Intermediate Representations - Compiles Rust source code into a PTX-based intermediate representation for GPU execution.
CUDA Accelerated Neural Networks - Implements hardware-optimized primitives to accelerate deep learning workloads on NVIDIA GPUs.
GPU Kernel Primitives - Provides highly tuned GPU kernels for standard deep learning operations tuned for tensor cores.
Raytracing Primitives - Interfaces directly with specialized GPU circuits to compute light paths and perform image denoising.
Raytracing Simulations - Uses specialized GPU circuits to implement physical light transport and path tracing simulations.
Real-Time Raytracing - Implements real-time raytracing techniques using specialized hardware circuits for physical light transport.
Hardware Driver API Mappings - Provides mappings that link high-level programmatic calls to native NVIDIA hardware driver and runtime APIs.
Binary Instruction Execution - Executes low-level binary instructions on graphics hardware using a specialized intermediate representation.
Compilation Target Specifications - Defines specific hardware compute capabilities during compilation to optimize machine code for target versions.
Hardware-Targeted Compilation - Configures compilation rules to optimize binaries for specific GPU hardware revisions and architectures.

Open-source alternatives to Rust Cuda

Similar open-source projects, ranked by how many features they share with Rust Cuda.

nvidia/cuda-python
NVIDIA/cuda-python
3,170View on GitHub
cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic wrapper for controlling device memory, managing hardware toolchains, and orchestrating execution graphs on NVIDIA GPUs, allowing for the compilation and launching of parallel kernels directly from Python. The project enables the development of SIMT kernels and the execution of mathematical algorithms on device memory. It integrates pre-compiled bytecode as custom operators and interfaces with accelerated device libraries to access low-level hardware functions without leaving the la
Cython
View on GitHub3,170
z-libs/zen-c
z-libs/Zen-C
3,819View on GitHub
Zen-C is a multi-target systems language and source-to-source compiler that translates high-level logic into human-readable GNU C or C11 code. It functions as a JIT-enabled programming language with an in-process compiler for real-time interactive code evaluation and testing. The project serves as a CUDA GPU kernel generator, mapping specialized syntax to CUDA C++ using device attributes to target graphics hardware. It acts as an interoperability layer capable of emitting compatible code for C++, Objective-C, and Lisp to bridge native system frameworks and libraries. The language includes an
Cccompilerlsp
View on GitHub3,819
answerdotai/gpu.cpp
AnswerDotAI/gpu.cpp
3,981View on GitHub
gpu.cpp is a lightweight C++ library for executing low-level general-purpose GPU computation across different hardware vendors and operating systems. It functions as a portable GPU wrapper, kernel orchestrator, and tensor management system using the WebGPU specification to abstract device initialization, buffer transfers, and compute shader dispatching. The library provides a framework for defining compute kernels from shader code and managing their asynchronous dispatch and synchronization. It enables the execution of cross-platform compute shaders and the orchestration of GPU tasks through
C++
View on GitHub3,981
nvidia/cuda-samples
NVIDIA/cuda-samples
9,319View on GitHub
This repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves as a GPGPU implementation guide and a parallel computing reference, providing code for using graphics hardware to perform general-purpose calculations and high-performance parallel processing. The project provides specific samples for GPU kernel development and resource management. These include demonstrations of multi-GPU communication, peer-to-peer memory access, and system hardware inspection to coordinate distributed GPU resources. The codebase covers a wide range of capa
C++cudacuda-driver-apicuda-kernels
View on GitHub9,319

See all 30 alternatives to Rust Cuda

Rust-GPUrust-cuda

Rust Cuda

Features

Open-source alternatives to Rust Cuda

NVIDIA/cuda-python

z-libs/Zen-C

AnswerDotAI/gpu.cpp

NVIDIA/cuda-samples

Frequently asked questions

Star history

Open-source alternatives to Rust Cuda

NVIDIA/cuda-python

z-libs/Zen-C

AnswerDotAI/gpu.cpp

NVIDIA/cuda-samples

Frequently asked questions