Search Results for "cuda"
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Showing 19 open source projects for "cuda"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will... -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
...It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. The communication can straightforwardly be hidden behind computation [1, 3] (how this can be done automatically when using ParallelStencil.jl is shown in; a general approach particularly suited for CUDA C applications is explained in. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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cuDF
GPU DataFrame Library
...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. -
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TIGRE
TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
...Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our aim is to provide a wide range of easy-to-use algorithms for the tomographic community "off the shelf". We would like to build a stronger bridge between algorithm developers and imaging researchers/clinicians by encouraging and supporting contributions from both sides to TIGRE. -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ... -
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GLScene
GLScene Engine for Delphi & C++Builder VCL Windows 32/64
GLScene is an OpenGL based 3D library for Delphi, C++Builder and Lazarus. It provides visual components and objects allowing description and rendering 3D scenes in graphic applications. Update of newer versions of the trunk has been moved to https://github.com/glscene -
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rayshade-mathematica
rayshade and POV for Mathematica Export + view
...Now very portable (Apple, Microsoft, Linux) supported (given rayshade or povray binary download or compiled). No OS scripting needed. * raytracing makes more realistic images than GL/CL/Cuda (are best for game, geo/mapping, accelerated) but is slower. POVRay is supported about as well. For a .gif animation see: http://community.wolfram.com/groups/-/m/t/1096001 see also: https://sourceforge.net/p/rayshade http://www.povray.org -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
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. ... -
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Flux3D.jl
3D computer vision library in Julia
...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. -
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CUDAnative.jl
Julia support for native CUDA programming
The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager. -
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CuArrays.jl
A Curious Cumulation of CUDA Cuisine
CuArrays provides a fully-functional GPU array, which can give significant speedups over normal arrays without code changes. CuArrays are implemented fully in Julia, making the implementation elegant and extremely generic. -
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Merlin.jl
Deep Learning for Julia
Merlin is a deep learning framework written in Julia. It aims to provide a fast, flexible and compact deep learning library for machine learning. Merlin is tested against Julia 1.0 on Linux, OS X, and Windows (x64). -
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OpenGL Mathematics (GLM) is a C++ mathematics library for 3D software based on the OpenGL Shading Language (GLSL) specification.
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GPU Edge Detector
GPU Interactive Program For Edge detection
...(The code section is currently close until our article gets published) Commiting publicly to the project is currently closed but feel free to email us. The source code is dependant on CUDA 5.0 Samples now available as part of the CUDA Toolkit. "This software contains source code provided by NVIDIA Corporation." -
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This project shows how to integrate NVIDIA CUDA GPU programming API into ITK (Insight Segmentation and Registration Toolkit) library
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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.
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