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gpu-guide

Graphics Processing Unit (GPU) Architecture Guide
https://github.com/mikeroyal/gpu-guide

Last synced: 5 days ago
JSON representation

  • MATLAB Tools, Libraries, Frameworks

    • SEA-MAT
    • Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
    • hctsa - series analysis using Matlab.
    • YALMIP
    • GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

  • Metal Learning Resources

  • Metal Tools, Libraries, and Frameworks

  • ML Frameworks, Libraries, and Tools

    • Amazon SageMaker
    • Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
    • Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
    • Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
    • Anaconda
    • PlaidML
    • OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
    • Scikit-Learn
    • Caffe
    • Theano - dimensional arrays efficiently including tight integration with NumPy.
    • nGraph - of-use to AI developers.
    • Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
    • Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
    • TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

  • OpenCL Learning Resources

  • OpenCL Tools, Libraries and Frameworks

    • GPUVerify
    • OpenCL ICD Loader
    • clBLAS
    • clFFT
    • clSPARSE
    • clRNG
    • CLsmith - core environment, OpenCL. Its primary feature is the generation of random OpenCL kernels, exercising many features of the language. It also brings a novel idea of applying EMI, via dead-code injection.
    • Oclgrind - races and barrier divergence, collecting instruction histograms, and for interactive OpenCL kernel debugging. The simulator is built on an interpreter for LLVM IR.
    • NVIDIA® Nsight™ Visual Studio Edition
    • Radeon™ GPU Profiler
    • Radeon™ GPU Analyzer
    • AMD Radeon ProRender - based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
    • Intel® SDK For OpenCL™ Applications - intensive workloads. Customize heterogeneous compute applications and accelerate performance with kernel-based programming.
    • NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
    • NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.

  • OpenGL Learning Resources

  • OpenGL Tools, Libraries, and Frameworks

    • BuGLe - like OSes. BuGLe combines a graphical OpenGL debugger with a selection of filters on the OpenGL command stream. The debugger allows viewing of state, textures, framebuffers and shaders, while the filters allow for logging, error checking, video capture and more.
    • gDEBugger - featured and free debugger and profiler representing the state-of-the-art in OpenGL and OpenGL ES debugging and profiling on Windows and Linux.
    • KTX
    • Equalizer
    • GLee - platform extension loading library that takes the burden off your application. GLee makes it easy to check for OpenGL extension and core version availability, automatically setting up the entry points with no effort on your part.
    • GLEW - source cross-platform extension loading library with thread-safe support for multiple rendering contexts and automatic code generation capability. GLEW provides easy-to-use and efficient methods for checking OpenGL extensions and core functionality.
    • libktx
    • OpenSceneGraph - level 3D graphics toolkit exposing OpenGL's capabilities while providing many capabilities of its own. OpenSceneGraph boasts a large user community and has been employed for visual simulation, games, virtual reality, scientific visualization, and modeling.
    • Mesa 3D Graphics Library - source implementation of the OpenGL specification. A system for rendering interactive 3D graphics. Mesa ties into several other open-source projects: the [Direct Rendering Infrastructure](https://dri.freedesktop.org/), [X.org](https://x.org/), and [Wayland](https://wayland.freedesktop.org/) to provide OpenGL support on Linux, FreeBSD, and other operating systems.

  • Parallel Computing Learning Resources

  • Parallel Computing Tools, Libraries, and Frameworks

    • Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
    • Statistics and Machine Learning Toolbox™
    • OpenMP - platform shared-memory parallel programming in C/C++ and Fortran. The OpenMP API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer.
    • CUDA®
    • Message Passing Interface (MPI) - passing standard designed to function on parallel computing architectures.
    • Slurm - source workload manager designed specifically to satisfy the demanding needs of high performance computing.
    • AWS ParallelCluster - supported open source cluster management tool that makes it easy for you to deploy and manage High Performance Computing (HPC) clusters on AWS. ParallelCluster uses a simple text file to model and provision all the resources needed for your HPC applications in an automated and secure manner.
    • Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
    • Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
    • cuML - learn.
    • Apache Flume
    • XGBoost
    • Apache Mesos
    • Apache HBase™ - source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
    • Hadoop Distributed File System (HDFS) - yarn/hadoop-yarn-site/YARN.html).
    • Apache Arrow - independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
    • Apache Spark™ - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
    • Apache PredictionIO
    • Microsoft Project Bonsai - code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
    • Cluster Manager for Apache Kafka(CMAK)
    • BigDL
    • Apache Beam - specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs).
    • Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
    • Neo4j - strength graph database that combines native graph storage, advanced security, scalable speed-optimized architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.
    • ElasticSearch - capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
    • Logstash
    • Kibana
    • Trino - us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
    • Redis(REmote DIctionary Server) - memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    • Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
    • Open Neural Network Exchange(ONNX) - in operators and standard data types.
    • Back to the Top
    • AutoGluon - accuracy deep learning models on tabular, image, and text data.

  • Performance Benchmarks

  • Python Frameworks and Tools

  • Python Learning Resources

  • R Learning Resources

  • R Tools, Libraries, and Frameworks

    • Code Server
    • R Debugger
    • Language Server Protocol (LSP)
    • Rmarkdown
    • R Host
    • Rplugin
    • Plotly
    • Metaflow - life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
    • MLR
    • ML workspace - in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
    • Plumber
    • DiagrammeR
    • Knitr - purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
    • Broom
    • Shiny
    • VSCode-R - project.org/), including features such as extended syntax highlighting, R language service based on code analysis, interacting with R terminals, viewing data, plots, workspace variables, help pages, managing packages, and working with [R Markdown](https://rmarkdown.rstudio.com/) documents.

  • Steam

  • Vulkan Learning Resources

  • Vulkan Tools, Libraries, and Frameworks

    • SPIRV-Reflect - V shader bytecode in Vulkan applications.
    • Vulkan® Tools
    • Vulkan-Hpp
    • Vulkan® Memory Allocator (VMA)
    • AMD Open Source Driver for Vulkan® - source Vulkan driver for AMD Radeon™ graphics adapters on Linux®.
    • Radeon™ Memory Visualizer (RMV)
    • DXVK - based translation layer for Direct3D 9/10/11 which allows running 3D applications on Linux using Wine.
    • PerfDoc - platform Vulkan layer which checks Vulkan applications for [best practices on Arm Mali](https://developer.arm.com/graphics/developer-guides/mali-gpu-best-practices) devices.
    • GLFW - platform library for OpenGL, OpenGL ES and Vulkan application development. It provides a simple, platform-independent API for creating windows, contexts and surfaces, reading input, handling events, etc. GLFW natively supports Windows, macOS and Linux and other Unix-like systems. On Linux both X11 and Wayland are supported.
    • VulkanSharp
    • Vortice.Vulkan - level bindings for Vulkan API.
    • VKD3D-Proton
    • ImGui - free graphical user interface library for C++. It outputs optimized vertex buffers that you can render anytime in your 3D-pipeline enabled application. It is fast, portable, renderer agnostic and self-contained (no external dependencies).
    • gfx-rs - level, cross-platform graphics and compute abstraction library in Rust.
    • Vulkan.jl
Programming Languages
C++ 27 Python 15 Julia 11 C 6 C# 4 MATLAB 3 HTML 2 Scala 2 Java 2 Rust 2
Categories
3D Graphics and Design Tools 47 Audio/Video Tools and Equipment 34 Parallel Computing Tools, Libraries, and Frameworks 33 Deep Learning Learning Resources 28 C/C++ Tools and Frameworks 28 C/C++ Learning Resources 27 CUDA Tools Libraries, and Frameworks 26 Python Frameworks and Tools 25 Deep Learning Tools, Libraries, and Frameworks 21 3D Graphics and Design Learning Resources 18 Julia Tools, Libraries and Frameworks 17 Game Development Tools, Libraries, and Frameworks 16 R Tools, Libraries, and Frameworks 16 OpenCL Tools, Libraries and Frameworks 15 Vulkan Tools, Libraries, and Frameworks 15 MATLAB Learning Resources 15 ML Frameworks, Libraries, and Tools 14 MATLAB Tools, Libraries, Frameworks 14 Parallel Computing Learning Resources 12 Computer Vision Learning Resources 10 Augmented Reality (AR) & Virtual Reality (VR) 10 Python Learning Resources 10 DirectX Learning Resources 9 OpenGL Tools, Libraries, and Frameworks 9 R Learning Resources 9 Core ML Tools, Libraries, and Frameworks 9 DirectX Tools, Libraries, and Frameworks 9 Julia Learning Resources 8 Game Development Learning Resources 8 OpenGL Learning Resources 8 Core ML Learning Resources 8 Audio/Video Learning Resources 8 OpenCL Learning Resources 7 Vulkan Learning Resources 7 Game Emulators 7 Game Engines 7 Learning Resources for ML 6 Metal Learning Resources 5 Game Streaming 5 CUDA Learning Resources 4 Metal Tools, Libraries, and Frameworks 4 Performance Benchmarks 3 Computer Vision Tools, Libraries, and Frameworks 3 Steam 2 Contribute 1 License 1 Apple Arcade 1
Sub Categories
Keywords
python 14 cpp 10 gpu 8 deep-learning 8 cuda 8 vulkan 7 machine-learning 6 julia 5 graphics 4 gamedev 4 nvidia 4 game-engine 4 game-development 4 data-science 4 neural-network 3 windows 3 pytorch 3 tensorflow 3 c 3 cplusplus 3 cxx14 3 matlab 3 cpp14 3 neural-networks 3 cross-platform 3 cpp11 3 dotnet 3 cpp17 2 algorithms 2 cpp20 2 azure 2 cxx 2 azure-sdk 2 cxx11 2 docker 2 iot 2 linux 2 cloud 2 metal 2 machine-learning-algorithms 2 vulkan-api 2 android 2 c-plus-plus 2 ios 2 data-visualization 2 numpy 2 nlp 2 java 2 rest 2 developer-tools 2