Search Results for "cpu"
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Showing 52 open source projects for "cpu"
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SwiftShader
SwiftShader is a high-performance CPU-based implementation
SwiftShader is Google’s high-performance CPU-based implementation of the Vulkan 1.3 graphics API, designed to provide a hardware-independent rendering solution for 3D graphics. Unlike traditional GPU drivers, SwiftShader executes graphics commands entirely on the CPU, making it ideal for environments where dedicated graphics hardware is unavailable or unsuitable. It acts as a drop-in replacement for Vulkan drivers, allowing existing applications to run seamlessly by redirecting API calls through its software-based rendering engine. ... -
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Ultralight
Lightweight, high-performance HTML renderer for game developers
...Official API for C and C++, with bindings for more. Render web-content on the GPU via Direct3D, Metal, OpenGL, or your own engine for unmatched visual performance. Render web-content on the CPU via SIMD/parallel for incredibly easy integration with any environment (including server-side!). Ultralight is engineered for peak performance, ensuring minimal CPU and memory usage. Customize low-level platform functionality, integrate JavaScript directly with native code, dive deep into performance tuning, and more. Built for maximum portability, optimized for PCs, game consoles, TVs, and embedded systems. -
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DXVK
Vulkan-based implementation of D3D9, D3D10 and D3D11 for Linux / Wine
...Currently however, there are still a few unsupported features, such as shared resources, predication, class linkage and target-independent rasterization. To get the best results out of this project, it is recommended that you use an esync-enabled Wine build to reduce CPU overhead in some games, and to disable desktop effects on your compositor, as this can cause stuttering issues when games are GPU-bound. -
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Upscayl
Free and Open Source AI Image Upscaler for Linux, MacOS and Windows
...This means that we prioritize Linux builds over others but that doesn't mean we'll break things for other OSes. Upscayl does not work without a GPU, sorry. You'll need a Vulkan-compatible GPU to upscale images. CPU or iGPU won't work. You can also download the flatpak version and double-click the flatpak file to install via Store but wait for the full release, we'll be pushing it to Flathub for easy access. Upscayl uses AI models to enhance your images by guessing what the details could be. It uses Real-ESRGAN (and more in the future) model to achieve this. ... -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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MESHROOM
3D reconstruction software
...Automatically estimate fisheye circle or manually edit it. Take advantage of motorized-head file. Easy to integrate in your Renderfarm System. Add specific rules to select the most suitable machines regarding CPU, RAM, GPU requirements of each Node. -
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tilemaker
Make OpenStreetMap vector tiles without the stack
Create the vector tiles on your home machine or using a cloud CPU. Upload the file to your site or app. And that's it. No database to maintain; no contract to pay; and no restriction on commercial use. tilemaker is a single executable that takes OpenStreetMap data and makes it into vector tiles. It's supremely customizable, but if you just want off-the-shelf tiles in a standard style, tilemaker comes bundled with the files to do that too. -
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Taichi
Productive, portable, and performant GPU programming in Python
Taichi is an open-source, embedded DSL within Python designed for high-performance numerical and physical simulations. It uses JIT compilation (via LLVM and its runtime TiRT) to offload compute-heavy code to CPUs, GPUs, mobile devices, and embedded systems. With built-in support for sparse data structures (SNode), automatic differentiation, AOT deployment, and compatibility with CUDA, Vulkan, Metal, and OpenGL ES, it empowers disciplines like simulation, graphics, AI, and robotics -
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ArrayFire
ArrayFire, a general purpose GPU library
...Together we can fulfill The ArrayFire Mission under an excellent Code of Conduct that promotes a respectful and friendly building experience. Rigorous benchmarks and tests ensuring top performance and numerical accuracy. Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux. Built-in visualization functions through Forge. -
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Generic Image Decoder
A standalone, portable generic Ada package for decoding images
The Generic Image Decoder is a package for decoding a broad variety of image formats, from any data stream, to any kind of medium. Unconditionally portable code: OS-, CPU-, compiler- independent code. More information on... http://gen-img-dec.sf.net Alire crate: https://alire.ada.dev/crates/gid Mirror: https://github.com/zertovitch/gid -
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LDPartEditor
Create and edit LDraw™ parts quick and easy.
...Minimum System Requirements: - OpenGL 2.1 compatible Graphics Card - Operating System (64-bit): Windows [7 or newer], Mac OS X [>=10.6], Linux [e.g. Ubuntu Linux >=14.4] - CPU: Multicore-Processor e.g. Intel Core 2 Duo or AMD Athlon II (>2.0Ghz) - RAM: 4 GB - Video-Memory: 1 GB - Free Disk Space: 150 MB Recommended Requirements: - OpenGL 3.3 compatible Graphics Card - Operating System (64-bit): Windows 7,8,10,11, Mac OS X [>=10.6], Linux [e.g. Ubuntu Linux >=14.4] - CPU: Multicore-Processor with 4 cores (or more) - RAM: >4 GB - Video-Memory: >1 GB - Free Disk Space: 512 MB The sourcecode is now hosted on GitHub here: http://nilsschmidt1337.github.io/ldparteditor/ -
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kgclock
Desktop Analog Clock for Linux and notification system
...The binary tarball should work for any 64 bit Linux distribution. If one prefers to build from source one need to install Kulina Graphics also. One specialty of 'kgclock' its low cpu usage, after one minute it is negligible. -
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...(Disclaimer: Processing time depends on compute hardware) Typical processing time for 100mb .las is about 2 seconds. 25mb .laz file process in about the same times. Files are processed in parallel using available CPU cores. Pre-built Windows x64 installer available at https://buymeacoffee.com/nathancrews/e/255641
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grafx - Extensible Java Graphics Package
Extensible Line-drawing & Polygon-filling Rasterization Libs for Java
Line-drawing and polygon-filling rasterization algorithms in Java. Simple 2D package and more complex 3D package with Z-buffering. Texture mapping not supported at the moment. Color interpolation for lines, Gouraud shading for polygons fully supported. Floating point vertex coordinates, not integer. Pixel-level aliasing, not endpoint aliasing. Doesn't use Bresenham line style! Works with all Java versions. No external dependencies. Suitable for study in academic institutions,... -
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SRM
C library for the development of Linux OpenGL DRM/KMS apps
SRM is a C library that simplifies the development of Linux DRM/KMS API applications. With SRM, you can focus on the OpenGL ES 2.0 logic of your application. For each available display, you can start a rendering thread that triggers common events like initializeGL(), paintGL(), resizeGL(), pageFlipped() and uninitializeGL(). SRM allows you to use multiple GPUs simultaneously and automatically finds the most efficient configuration. It also offers functions for creating OpenGL textures,... -
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Forma
An efficient vector-graphics renderer
Forma is an experimental vector graphics renderer written in Rust, developed by Google to explore high-performance, parallelized rendering techniques across multiple platforms. The project aims to achieve portability, performance, simplicity, and small footprint through a streamlined four-stage rendering pipeline. Forma provides both CPU (software) and GPU (hardware) backends, relying on Rust’s SIMD auto-vectorization, Rayon for multithreading, and WebGPU (wgpu) for hardware acceleration. The renderer processes Bézier curves, line segments, and pixels through stages of flattening, rasterization, sorting, and painting, updating only changed tiles for efficiency. This design allows Forma to render complex vector scenes—such as large-scale SVGs—at interactive frame rates even on CPUs. -
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GroIMP
Growth-grammar related Interactive Modelling Platform
Important: Groimp migrates to Gitlab. You can find the latest code at "https://gitlab.com/grogra/groimp/". The version on Sourceforge will not be updated anymore. The modelling platform GroIMP is designed as an integrated platform which incorporates modelling, visualisation and interaction. It exhibits several features which makes itself suitable for the field of biological or ALife modelling: The “modelling backbone” consists in the language XL. It is fully integrated, e.g., the... -
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Skija
Java bindings for Skia
Skija is a high-performance, Java bindings library for the Skia graphics engine, allowing JVM and Kotlin applications to access the full capabilities of Skia’s 2D GPU-accelerated graphics without writing native code. Skia is the same graphics engine used in Chrome, Android, Flutter, and other platforms, and Skija leverages this robust foundation to provide fast rendering of paths, text, images, transformations, filters, and animations within desktop and embedded Java environments. The... -
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3DDFA
Fast, accurate and stable 3D dense face alignment
...A simple 3D render written by c++ and cython is also included. This repo supports the onnxruntime, and the latency of regressing 3DMM parameters using the default backbone is about 1.35ms/image on CPU with a single image as input. See requirements.txt, tested on macOS and Linux platforms. The Windows users may refer to FQA for building issues. Note that this repo uses Python3. The major dependencies are PyTorch, numpy, opencv-python and onnxruntime, etc. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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Simd
High performance image processing library in C++
...It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. The library supports dynamic and static linking, 32-bit and 64-bit Windows, Android and Linux, MSVS, G++ and Clang compilers, MSVS project and CMake build systems. -
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Skinner
Special Effects with Skinned Mesh in Unity
Skinner is a collection of real-time special effects for Unity that use vertices of an animating skinned mesh as emission points. Instead of duplicating mesh data on the CPU, it employs a replacement shader to stream vertex positions into GPU-friendly buffers, conserving memory and CPU cycles. With those GPU-side buffers, Skinner can drive effects like trails, particles, or geometry that react to the underlying skinned animation in sophisticated ways. The approach enables complex, performant visuals that stay in sync with character motion. ... -
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viewpix
A Landsat 8 scene viewer
...Viewpix runs efficiently on computers with modest CPU's such as low power mini-ITX boards. It is known to work nicely on Intel NUC, AMD A6 SOC , and NVIDIA TX2. A multiple core cpu, 8 GB ram, and a large capacity hard drive are suggested. -
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Keras.js
Run Keras models in the browser, with GPU support using WebGL
Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. Check out the demos/ directory for real examples running Keras.js in VueJS. Library version compatibility, Keras 2.1.2. -
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waifu2x-cpu-torch-vks
waifu2x fork without CUDA
The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use). -
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SFML Voxel Raycaster
Simple voxel raycasting engine in SFML with multithreading support
Voxel raycasting engine written in C++ drawed by SFML, curently in alpha preview state. Currently rays are computed by CPU and performance is low and far away from realtime.
