# rust-gpu/rust-cuda **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/rust-gpu-rust-cuda).** 5,245 stars · 237 forks · Rust · Apache-2.0 ## Links - GitHub: https://github.com/Rust-GPU/rust-cuda - awesome-repositories: https://awesome-repositories.com/repository/rust-gpu-rust-cuda.md ## Topics `cuda` `cuda-kernels` `cuda-programming` `gpgpu` `gpu` `gpu-programming` `rust` `rust-lang` ## Description 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++. The project includes a compute library with hardware-optimized primitives for neural network acceleration and hardware-accelerated raytracing. It utilizes a compilation toolchain that translates source code into a low-level intermediate representation for execution on graphics processors. The framework covers device resource management, kernel development, and the simulation of high-precision integer operations. It also supports device-side random number generation and target-specific compute optimizations. Pre-configured container images are available to simplify the provisioning of the compiler toolchain and development environment across different hardware architectures. ## Tags ### Programming Languages & Runtimes - [GPU Compilers](https://awesome-repositories.com/f/programming-languages-runtimes/compiler-interpreter-internals/compiler-toolchains/gpu-compilers.md) — Provides a complete toolchain that translates Rust source code into optimized NVIDIA GPU hardware instructions. ([source](https://rust-gpu.github.io/rust-cuda/)) - [GPU Kernel Programming](https://awesome-repositories.com/f/programming-languages-runtimes/gpu-kernel-programming.md) — Provides a framework for writing and executing high-performance GPU kernels using Rust. - [CUDA Kernel Compilers](https://awesome-repositories.com/f/programming-languages-runtimes/compiler-interpreter-internals/compiler-infrastructure/jit-kernel-compilers/cuda-kernel-compilers.md) — Compiles GPU source code into target-specific CUDA kernels with a Rust interface. - [GPU Compute Toolchains](https://awesome-repositories.com/f/programming-languages-runtimes/gpu-compute-toolchains.md) — Provides an integrated development environment and compiler for writing high-performance Rust compute kernels. - [Intermediate Representations](https://awesome-repositories.com/f/programming-languages-runtimes/machine-code-generation/intermediate-representations.md) — Compiles Rust source code into a PTX-based intermediate representation for GPU execution. - [Binary Instruction Execution](https://awesome-repositories.com/f/programming-languages-runtimes/binary-instruction-execution.md) — Executes low-level binary instructions on graphics hardware using a specialized intermediate representation. ([source](https://rust-gpu.github.io/blog/2025/01/27/rust-cuda-reboot)) - [Compilation Target Specifications](https://awesome-repositories.com/f/programming-languages-runtimes/compilation-target-specifications.md) — Defines specific hardware compute capabilities during compilation to optimize machine code for target versions. - [Hardware-Targeted Compilation](https://awesome-repositories.com/f/programming-languages-runtimes/source-code-compilers/multi-target-compilers/hardware-targeted-compilation.md) — Configures compilation rules to optimize binaries for specific GPU hardware revisions and architectures. ([source](https://rust-gpu.github.io/blog/2025/08/11/rust-cuda-update)) ### Graphics & Multimedia - [Compute Libraries](https://awesome-repositories.com/f/graphics-multimedia/gpu-hardware-acceleration/compute-libraries.md) — Ships a compute library with hardware-optimized primitives for neural networks and raytracing. - [GPU Resource Management](https://awesome-repositories.com/f/graphics-multimedia/gpu-resource-management.md) — Implements a high-level driver interface to manage the lifecycle and allocation of GPU buffers and kernels. ([source](https://rust-gpu.github.io/rust-cuda/)) - [Raytracing Primitives](https://awesome-repositories.com/f/graphics-multimedia/gpu-hardware-acceleration/raytracing-primitives.md) — Interfaces directly with specialized GPU circuits to compute light paths and perform image denoising. - [Raytracing Simulations](https://awesome-repositories.com/f/graphics-multimedia/physically-based-lighting/raytracing-simulations.md) — Uses specialized GPU circuits to implement physical light transport and path tracing simulations. - [Real-Time Raytracing](https://awesome-repositories.com/f/graphics-multimedia/real-time-neural-renderers/real-time-3d-rendering-engines/real-time-raytracing.md) — Implements real-time raytracing techniques using specialized hardware circuits for physical light transport. ([source](https://rust-gpu.github.io/rust-cuda/)) ### Operating Systems & Systems Programming - [CUDA Driver Wrappers](https://awesome-repositories.com/f/operating-systems-systems-programming/cuda-driver-wrappers.md) — Provides a programmatic abstraction for controlling NVIDIA GPU device memory and kernel execution. - [GPU Kernel Development](https://awesome-repositories.com/f/operating-systems-systems-programming/gpu-kernel-development.md) — Provides a framework for managing thread indices and memory allocation to create device-side logic. ([source](https://rust-gpu.github.io/rust-cuda/)) ### Artificial Intelligence & ML - [CUDA Accelerated Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/cuda-accelerated-neural-networks.md) — Implements hardware-optimized primitives to accelerate deep learning workloads on NVIDIA GPUs. ([source](https://rust-gpu.github.io/rust-cuda/)) ### Part of an Awesome List - [GPU Kernel Primitives](https://awesome-repositories.com/f/awesome-lists/devtools/gpu-acceleration/deep-learning-acceleration/gpu-kernel-primitives.md) — Provides highly tuned GPU kernels for standard deep learning operations tuned for tensor cores. ### Hardware & IoT - [Hardware Driver API Mappings](https://awesome-repositories.com/f/hardware-iot/driver-to-device-mapping/hardware-driver-api-mappings.md) — Provides mappings that link high-level programmatic calls to native NVIDIA hardware driver and runtime APIs.