Search Results for "gpu"

...Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code.

Building blocks for invertible neural networks in the Julia programming language.

This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units)....

...Change-log for MathMod-11.1 (16/06/2021) 1) Support of graphing functions with complex numbers Z=u+iv in 3D and 4D spaces (demo scripts: "Complex3D_xx" and "Complex4D_Saddle") 2) Added support for HSV (hue, saturation, brightness) coloring model (script: "Complex_Domain_Coloring") 3) Texture definitions (RGB and HSV) for parametric surfaces can now be expressed as functions of complex numbers 4) Added a software implementation for the OpenGL variable gl_FrontFacing (fixed an issue with GPU drivers on MacOSX Catalina). The activation of this patch is available from mathmodconfig.js ("OpenGlConfig"-->"GL_FrontFacingSupport"=false) or the GUI("Appearance"-->"OpenGL Front Surface Support") 5) Bug-fixes, code optimization and documentation update.

VirtualGL redirects 3D commands from a Unix/Linux OpenGL application onto a server-side GPU and converts the rendered 3D images into a video stream with which remote clients can interact to view and control the 3D application in real time.

...This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl.

...Experiments on aligning 150bp paired-end reads show that MICA using one MIC board is ~4.85 times faster than the CPU-(multi-core)-based BWA-MEM and about the same speed as the GPU-based SOAP3-dp. Furthermore, MICA’s simplicity allows very efficient scale-up when multiple MIC boards are used in a node (3 cards gives a 14-fold speedup over 6-core BWA-MEM).

This is a standalone, all in one Edge Detector that is mainly running on GPU using the CUDA technology. Each algorithm also has an OpenCV counterpart which is the closest one in terms of speed. This project is partly based on the Sobel Filter sample available as part of the CUDA SDK. This project also partly uses the OpenCV library to load different types of image. This program includes: Prewitt filter, Roberts Cross, Canny edge detector and finally Sobel filter with the ability to modify some variables such as sigma in Canny and see the result in real-time. ...

Equalizer is the standard middleware to create parallel OpenGL-based applications. Please visit https://github.com/Eyescale for current development information.

multiDAC is intended to become a user-friendly tool for image- and videoprocessing in the field of deformation/movement analysis. It is written in C# with some C routines using CPU/GPU parallelization (e.g. CUDA) and features a plugin manager.

An OS/hardware vendor independent GPU accelerated image and video processing library written in C. Interface allows easy combination and manipulation of customizable filters. Works with an active OpenGL context.