Search Results for "cushaw2-gpu"
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Showing 211 open source projects for "cushaw2-gpu"
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NVIDIA GPU Operator
NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes
...However, configuring and managing nodes with these hardware resources requires the configuration of multiple software components such as drivers, container runtimes or other libraries which are difficult and prone to errors. The NVIDIA GPU Operator uses the operator framework within Kubernetes to automate the management of all NVIDIA software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the NVIDIA Container Runtime, automatic node labeling, DCGM-based monitoring, and others. -
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nviwatch
A blazingly fast rust based TUI for managing and monitoring NVIDIA GPU
NviWatch is an interactive terminal user interface (TUI) application for monitoring NVIDIA GPU devices and processes. Built with Rust, it provides real-time insights into GPU performance metrics, including temperature, utilization, memory usage, and power consumption. -
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ComputeSharp
.NET library to run C# code in parallel on the GPU through DX12
ComputeSharp is a .NET library to run C# code in parallel on the GPU through DX12 and dynamically generated HLSL compute shaders. The available APIs let you access GPU devices, allocate GPU buffers and textures, move data between them and the RAM, write compute shaders entirely in C# and have them run on the GPU. The goal of this project is to make GPU computing easy to use for all .NET developers! -
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Numba CUDA Target
The CUDA target for Numba
Numba CUDA Target is NVIDIA’s maintained CUDA backend for the Numba JIT compiler, enabling developers to write GPU-accelerated code directly in Python. It allows users to define CUDA kernels using Python syntax, which are then compiled into efficient GPU code at runtime using LLVM-based toolchains. This approach significantly lowers the barrier to entry for GPU programming by eliminating the need to write CUDA C++ while still delivering high performance. -
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Numbast
Build an automated pipeline that converts CUDA APIs into Numba
Numbast is an automated toolchain that bridges CUDA C++ and Python by generating Numba-compatible bindings directly from CUDA header files. Its primary goal is to eliminate the manual effort required to expose CUDA libraries to Python, enabling developers to use GPU-accelerated functionality in Python environments more easily. The system parses CUDA C++ declarations and converts them into Python bindings that can be used within Numba, allowing seamless integration with Python-based GPU workflows. This approach significantly improves developer productivity by reducing boilerplate code and ensuring consistency between C++ and Python interfaces. ... -
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CuPy
A NumPy-compatible array library accelerated by CUDA
CuPy is an open source implementation of NumPy-compatible multi-dimensional array accelerated with NVIDIA CUDA. It consists of cupy.ndarray, a core multi-dimensional array class and many functions on it. CuPy offers GPU accelerated computing with Python, using CUDA-related libraries to fully utilize the GPU architecture. According to benchmarks, it can even speed up some operations by more than 100X. CuPy is highly compatible with NumPy, serving as a drop-in replacement in most cases. CuPy is very easy to install through pip or through precompiled binary packages called wheels for recommended environments. ... -
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waifu2x ncnn Vulkan
waifu2x converter ncnn version, run fast GPU with vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework. -
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Triton
Development repository for the Triton language and compiler
...The project leverages LLVM and MLIR to compile code into efficient GPU instructions, supporting both NVIDIA and AMD hardware. It is widely used in research and production environments where custom tensor operations are required, offering both high performance and developer-friendly syntax. -
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Khronos KTX
KTX (Khronos Texture) Library and Tools
KTX-Software is a suite of tools and libraries for working with Khronos Texture (KTX) files, designed and maintained by the Khronos Group. KTX is a container format for storing textures that are optimized for GPU upload, supporting modern formats like Basis Universal and ASTC. This repository includes tools for creating, validating, inspecting, and converting KTX and KTX2 files, making it essential for developers working in 3D engines, games, and visualization tools where texture streaming and compression are key. -
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CUDA Python
Performance meets Productivity
CUDA Python is a unified Python interface for accessing and working with the NVIDIA CUDA platform, enabling developers to build GPU-accelerated applications entirely in Python. It acts as a metapackage composed of multiple submodules that provide both high-level and low-level access to CUDA functionality, including runtime APIs, driver APIs, and JIT compilation tools. The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of CUDA for advanced users. ... -
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CubeCL
Multi-platform high-performance compute language extension for Rust
CubeCL is a low-level compute language and compiler framework designed to simplify and optimize GPU programming for high-performance workloads, particularly in machine learning and numerical computing. It provides an abstraction layer that allows developers to write portable, hardware-efficient compute kernels without directly dealing with complex GPU APIs such as CUDA or OpenCL. CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. ... -
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CUDA Core Compute Libraries (CCCL)
CUDA Core Compute Libraries
...By unifying these components, CCCL reduces duplication and improves developer productivity while maintaining performance across different GPU architectures. -
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Lapce
Lightning-fast and Powerful Code Editor written in Rust
Lapce is a GUI-based, next‑generation code editor written in Rust, using native GPU-accelerated rendering (via Floem and wgpu). It aims to deliver VS Code–level productivity with minimal latency, built-in LSP support, modal editing, remote development capabilities, and WASI‑based plugin extensibility. -
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Starling Framework
2D GPU-accelerated framework for ActionScript developers
Starling is an open-source 2D framework for ActionScript developers that leverages GPU acceleration via Adobe's Stage3D API to create smooth, high-performance games and applications across desktop and mobile platforms. It mimics the traditional Flash display list while dramatically improving performance, making it a popular choice for Flash developers transitioning into more efficient, hardware-accelerated environments. -
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SwissGL
SwissGL is a minimalistic wrapper on top of WebGL2 JS API
SwissGL is a compact JavaScript library that provides a streamlined abstraction layer over the WebGL2 API, designed to minimize boilerplate when building GPU-accelerated graphics, simulations, and procedural visualizations. Acting as a "Swiss Army knife" for WebGL2, it simplifies shader, texture, and framebuffer management into a single, expressive interface that enables developers to write complex GPU workflows in just a few lines of code. The library centers around one main function that unifies rendering and compute operations, allowing the creation of particle systems, GPGPU effects, and real-time simulations entirely on the GPU. ... -
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Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
...Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu and Ali to complete text recognition locally. Support GPU acceleration, after GPU acceleration, you can get higher accuracy and faster extraction speed. (CLI version) No need for users to manually set the subtitle area, the project automatically detects the subtitle area through the text detection model. Filter the text in the non-subtitle area and remove the watermark (station logo) text. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. ... -
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DGL
Python package built to ease deep learning on graph
...We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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PortableGL
An implementation of OpenGL 3.x-ish in clean C
PortableGL is a single-header, software-only implementation of a subset of OpenGL (specifically the GL 2.1 pipeline), designed to run entirely on the CPU. This lightweight graphics library allows OpenGL-style rendering without GPU acceleration, making it ideal for educational use, debugging, embedded systems, and retro-style software rendering. Because it mirrors OpenGL syntax and design, it can act as a drop-in CPU renderer for testing or deploying 3D graphics on platforms without GPU support. -
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NVIDIA AI Cluster Runtime (AICR)
Tooling for optimized and reproducible GPU-accelerated AI runtime
...Based on its positioning within NVIDIA’s repositories, it is designed to support scalable AI runtime environments, potentially addressing challenges related to orchestration, resource management, or reproducible AI execution. The project likely aligns with NVIDIA’s broader strategy of building modular infrastructure layers that integrate with GPU-accelerated workloads and cloud-native systems. It appears to emphasize automation, consistency, and performance optimization across AI pipelines, potentially targeting enterprise and research use cases. Given NVIDIA’s ecosystem, it may also integrate with containerized environments, Kubernetes, or other orchestration frameworks. -
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noUiSlider
JavaScript range slider with multi-touch and keyboard support
...It can be used for free and without any attribution, in any personal or commercial project. An extensive documentation, including examples, options and configuration details, is available in the website. GPU animated, no reflows, so fast; even on older devices. All modern browsers and IE > 9 are supported. -
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DeepSeed
Deep learning optimization library making distributed training easy
...DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. ... -
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MuJoCo Playground
An open source library for GPU-accelerated robot learning
MuJoCo Playground, developed by Google DeepMind, is a GPU-accelerated suite of simulation environments for robot learning and sim-to-real research, built on top of MuJoCo MJX. It unifies a range of control, locomotion, and manipulation tasks into a consistent and scalable framework optimized for JAX and Warp backends. The project includes classic control benchmarks from dm_control, advanced quadruped and bipedal locomotion systems, and dexterous as well as non-prehensile manipulation setups. ... -
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DALI
A GPU-accelerated library containing highly optimized building blocks
...These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference. DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. Additionally, DALI relies on its own execution engine, built to maximize the throughput of the input pipeline. -
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Zed
High-performance, multiplayer code editor from the creators of Atom
Zed is a next-generation code editor designed for high-performance collaboration with humans and AI. Written from scratch in Rust to efficiently leverage multiple CPU cores and your GPU. Integrate upcoming LLMs into your workflow to generate, transform, and analyze code. Chat with teammates, write notes together, and share your screen and project. Multibuffers compose excerpts from across the codebase in one editable surface. Evaluate code inline via Jupyter runtimes and collaboratively edit notebooks. Support for many languages via Tree-sitter, WebAssembly, and the Language Server Protocol. ...
