Search Results for "gpu max performance" - Page 4
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Showing 106 open source projects for "gpu max performance"
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Big Sleep
A simple command line tool for text to image generation
...You can set the number of classes that you wish to restrict Big Sleep to use for the Big GAN with the --max-classes flag as follows (ex. 15 classes). This may lead to extra stability during training, at the cost of lost expressivity. -
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feathersui-starling
User interface components for Starling Framework, ActionScript 3
Feathers UI (Starling edition) is a lightweight, open-source library of user interface components designed specifically for use with the Starling Framework. It allows ActionScript developers to build GPU-accelerated interfaces for games and applications that run on desktop and mobile platforms. With a focus on performance and flexibility, Feathers UI includes buttons, sliders, lists, navigators, and layout containers optimized for Starling's rendering pipeline. -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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sc
Common libraries and data structures for C
Portable, stand-alone C libraries and data structures. Each folder is stand-alone with a single header/source pair in it. There is no build for libraries, just copy files you want. e.g If you want the logger, copy sc_log.h and sc_log.c to your project. High performance & minimal memory usage. Portability between many operating systems and architectures. Tests with 100% branch coverage and multiple sanitizers. Drag & drop source code distribution. There is 100% branch coverage on Linux.... -
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macOS Simple KVM
Tools to set up a quick macOS VM in QEMU, accelerated by KVM
...The repository includes tools for preparing installation media, configuring virtual hardware, and managing VM launch scripts. By using KVM acceleration, the virtual machine runs with near-native performance, making it useful for testing, development, or personal experimentation. The project also supports GPU passthrough and other advanced configurations for users who want a more optimized macOS VM environment. While primarily intended for educational and testing purposes, it demonstrates how macOS can be virtualized outside of Apple hardware. -
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Tone.js
A Web Audio framework for making interactive music in the browser
...It has common DAW (digital audio workstation) features for those looking to schedule events and tinker with pre-built synths and effects. There’s also a great selection of high-performance building blocks for signal-processing programmers familiar with languages like Max/MSP. With Tone.js they can create their own synthesizers, effects, and complex control signals. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...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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Effeckt.css
A Performant Transitions and Animations Library
Effeckt.css is a showcase and toolkit of high-performance UI transitions and animations for the web. It catalogs common interaction patterns—button states, list reveals, modals, off-canvas menus, and page transitions—implemented with CSS transforms and opacity for smooth, GPU-friendly motion. The project focuses on practical details such as using compositing-friendly properties, keeping DOM structures lean, and providing hooks so JavaScript can toggle classes without micromanaging animation state. ... -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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Mocha.jl
Deep Learning framework for Julia
...It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. ... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. ... -
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PixelFlow
A Processing/Java library for high performance GPU-Computing (GLSL)
PixelFlow is a Processing library focused on advanced graphics and visual effects, offering an extensive suite of GPU-based tools for visual artists, researchers, and creative coders. It enables real-time simulation and rendering of complex effects such as fluid dynamics, reaction-diffusion systems, soft shadows, and more, all powered by GLSL shaders. Its modular structure allows for chaining and composing various visual effects easily, making it ideal for installations, performances, and... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
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SoAx
Structure of Arrays of multiple types
Structures of arrays (SoA) are generally faster than arrays of structures (AoS) while AoS are more handy. This project (SoAx) combines the advantages of both. By means of C++(11) meta-template programming SoAx achieves maximal performance (efficient use of vector units and cache of modern CPUs) while providing a very convenient user interface (including object-oriented element handling) and flexibility. It has been designed to handle list-like sets of particles (similar to struct {int id;... -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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Starling Extension Graphics
flash.display.Graphics style extension for the Starling Flash GPU
Starling-Extension-Graphics is an extension for the Starling framework (which itself is a GPU-accelerated 2D framework for Flash/AIR via Stage3D). This extension adds graphics primitives (fills, strokes, planes etc.) that mimic flash.display.Graphics-style drawing but implemented in a GPU-friendly manner. It automatically triangulates vector shapes, letting developers use familiar drawing APIs but get performance benefits of GPU rendering via Starling. ... -
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Starling Extension Particle System
A particle system for the Starling framework
The Starling Extension Particle System is an ActionScript extension for the Starling framework that enables developers to integrate particle effects created with the "Particle Designer" tool by 71squared into Starling-based applications. The demo-directory contains a sample project. To compile it, add a reference to the Starling library and add the source directory that contains the particle system classes. The project contains 4 sample configurations. Switch between configurations in... -
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ND2D
A Flash Molehill (Stage3D) GPU accelerated 2D game engine
ND2D is a 2D game framework for Flash that uses Stage3D / Molehill (i.e. the GPU acceleration in newer Flash Player versions). It allows game developers to build 2D games with lots of sprites, leveraging GPU for better performance. It includes display tree constructs, sprite sheets, particle systems, cameras, post-processing etc., made to simplify building high-performance 2D content in Flash. ND2D was built to make an ease use of hardware accelerated 2D content in the Flashplayer. ... -
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HTML GL
Get as many FPS as you need and amazing effects by rendering HTML/CSS
60 FPS and amazing effects by rendering HTML/CSS in WebGL, framework agnostic. HTML GL solves "the slow DOM problem" by creating WebGL representations of DOM elements and hiding actual DOM after. This speeds up HTML/CSS animations and transformations by using 3D hardware acceleration and allows to apply OpenGL effects as modern 3D games have. Using HTML GL you still work with HTML/CSS as you are common to, but DOM elements are just facades to their WebGL representations. These GPU... -
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WebGL Heatmap
A high performance WebGL/JS heatmap display library
webgl-heatmap is a browser-side rendering library that uses the GPU via WebGL to draw smooth, continuous heatmaps from large numbers of data points. Instead of relying on CPU-bound canvas operations, it leverages fragment shaders and additive blending to accumulate intensity and colorize results in real time. The API lets you push points or weighted samples into a buffer and then renders a gradient map where hot areas emerge organically from density rather than discrete markers. Because most... -
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MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++.
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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Java/C Comparative Benchmarks
Java and C Comparative Performance Benchmarks
A collection of software benchmarks developed to compare the performance of Java with C on identical code. No language libraries were used to avoid implementation differences. Some of the benchmarks are also implemented in Python and Scala. There are benchmarks for bit twiddling, numerical computing, data structure manipulation, concurrent computing, callouts to native libraries, and, graphics processing units (GPU) utilization. -
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StarlingPunk
StarlingPunk is a framework built on top the Starling library
StarlingPunk is a game framework built on top of the Starling GPU-accelerated 2D library (AS3 / Flash / AIR). It is inspired by FlashPunk: it gives structure (entities, worlds), collision detection systems, tile maps, etc., and is intended to help developers organize 2D game code more cleanly while benefiting from Starling’s performance. It has features for quick prototyping and reusing code between projects.
