gpu
gpu-io is a GPU-accelerated computing library for physics simulations and other mathematical calculations.
Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
Tile Language (tile-lang) is a concise domain-specific language designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.
Up to 100x faster strings for C, C++, CUDA, Python, Rust, Swift, JS, & Go, leveraging NEON, AVX2, AVX-512, SVE, GPGPU, & SWAR to accelerate search, hashing, sorting, edit distances, sketches, and memory ops 🦖
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Open, Device Virtualization, VGPU, Heterogeneous AI Computing.
HAMi (Heterogeneous AI Computing Virtualization Middleware) formerly known as k8s-vGPU-scheduler, is an 'all-in-one' chart designed to manage Heterogeneous AI Computing Devices in a k8s cluster. It can provide the ability to share Heterogeneous AI devices and provide resource isolation among tasks.
Linux GPU Configuration And Monitoring Tool.
This application allows you to control your AMD, Nvidia or Intel GPU on a Linux system.
🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
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Sirius is a GPU-native SQL engine. It plugs into existing databases such as DuckDB via the standard Substrait query format, requiring no query rewrites or major system changes. Sirius currently supports DuckDB and Doris (coming soon), other systems marked with * are on our roadmap.
Powerful CPU+GPU Programming. Mojo is a pythonic language for blazing-fast CPU+GPU execution without CUDA. Optionally use it with MAX for insanely fast AI inference.
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A GPU compute-centric 2D renderer.
Vello is a 2D graphics rendering engine written in Rust, with a focus on GPU compute. It can draw large 2D scenes with interactive or near-interactive performance, using wgpu for GPU access.
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The Cloud Built for AI.
Train, fine-tune and deploy AI models with RunPod.
Globally distributed GPU cloud for your AI workloads. Deploy any GPU workload seamlessly, so you can focus less on infrastructure and more on running ML models.
Rent GPUs.
Vast.ai is the market leader in low-cost cloud GPU rental. Use one simple interface to save 5-6X on GPU compute.
We wrote this glossary to solve a problem we ran into working with GPUs here at Modal : the documentation is fragmented, making it difficult to connect concepts at different levels of the stack, like Streaming Multiprocessor Architecture , Compute Capability , and nvcc compiler flags .
Solve Puzzles. Learn Metal 🤘.
Port of srush/GPU-Puzzles to Metal using MLX Custom Kernals.
GPUs are crucial in machine learning because they can process data on a massively parallel scale. While it's possible to become an expert in machine learning without writing any GPU code, building intuition is challenging when you're only working through layers of abstraction. Additionally, as models grow in complexity, the need for developers to write efficient, high-performance kernels becomes increasingly important to leverage the power of modern hardware.
An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.
GPU Accelerated JavaScript.
GPU.js is a JavaScript Acceleration library for GPGPU (General purpose computing on GPUs) in JavaScript for Web and Node. GPU.js automatically transpiles simple JavaScript functions into shader language and compiles them so they run on your GPU. In case a GPU is not available, the functions will still run in regular JavaScript.
Similar to the incredibly useful sleep/suspend function found in consoles like the Nintendo Switch and Sony PlayStation; suspend your game (and its resource usage) at any time, and resume whenever you wish - at the push of a button.