Search Results for "cpu benchmark linux"

IOPaint is a powerful open-source image editing tool focused on inpainting, outpainting, object removal, and general image manipulation driven by state-of-the-art AI models, delivering these capabilities through both local and hosted workflows. Designed to be fully self-hosted and flexible, IOPaint supports a variety of underlying generators and inpaint models — from LaMa erase networks to Stable Diffusion-based replace/object generation — giving users multiple ways to refine or reconstruct...

Fire Animation in JavaScript code inserted in HTML using gradient. ~80 lines code, 2 functions, run onload. fascinating fact, LLM (AI) Local or free online, is not able to make some like this, just fly orange or green balls (2026). I tested All to size 122B and all free online. PS: I found out that CPU only and Safari (MacOS) cannot handle this old style, so I made a modified version (_safari_optimized) that works with a texture.

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....

GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. 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. ...

...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.