Search Results for "gpu"

...The RAPIDS suite of open-source software libraries aims to enable the execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers.

...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 repo is an optimized CUDA version of FIt-SNE algorithm with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. Time taken compared to other state of the art algorithms on synthetic datasets with 50 dimensions and four clusters for varying numbers of points. ...

...Built in C++ with a modular, extensible architecture, Inviwo combines a visual editor (for creating data pipelines) with a powerful runtime engine that supports real-time rendering, interaction, and GPU-accelerated processing. It’s widely used in scientific domains for building and sharing visualizations of complex data such as medical imaging, simulations, and machine learning models. The platform supports both novice users through its graphical interface and advanced users through scripting and plugin development.

The objective of this project is to create a library of code for pricing financial derivatives products using CUDA to achieve GPU programming.

cleap is a a library for handling / processing / rendering 3D meshes. It uses GPU accelerated methods.

...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).

A SQL based analytic engine running on an NVIDIA GPU for exceptional performance. We see over 700x performance increase over a well known database on the same machine.

Ezys is a non-linear 3D medical image registration program. Ezys fully exploits the parallel computing power of inexpensive commercial graphics processing units (GPU), resulting in a very fast and accurate program capable of running on desktop PCs and even some laptops. On these systems, non-linear image registrations take less than a minute to complete. Ezys implements a diffeomorphic inverse consistent image registration algorithm with a demons-style regularization based on a non-parametric free form deformation model. ...

A shareware for build realtime photoacoustic tomography image using NVidia Geforce GPU. Can work in both offline mode or online mode.

We present an improved ball and stick representation called HyperBalls. This type of depiction is particularly useful to represent dynamic phenomena, such as the evolution of non covalent bonds and takes advantage of GPU capabilities. HyperBalls are now fully integrated into the UnityMol software and are actively developed in that context. You will find the most up-to-date versions of the shaders in the http://unitymol.sourceforge.net project.

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

VizStack is a framework to run GPU-enabled applications. You may set up tiled displays, and run popular applications on them. VizStack can convert nodes into a shared remote visualization farm. Individual GPUs can be allocated to users too.

This project implements several famous fractals, on GPU-Shaders. This enables the fractals to be calculated in real-time.

Volcast is a small, flexible and fast volume raycaster. Raycasting is done entirely on the GPU (nv6800+). The featureset includes: pre-integration, real-time editing of transfer-function and support for 8/16Bit volumes.

The Hardware Assisted Visibility Sorting (HAVS) algorithm is a GPU-based, direct volume renderer for unstructured grids. The algorithm operates in both object- and images-space and includes a sample-based, dynamic level-of-detail algorithm.

A short implementation of SVM related algorithms running on GPU