Parallel Programming with Microsoft .NET

•    CSharp

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Code samples for the patterns & practices book on design patterns for parallel programming, Parallel Programming with Microsoft .NET.

parallel parallel-programming design-patterns patterns

Parallel Programming with Microsoft Visual C++

•    C++

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Code samples for the patterns & practices book on design patterns for parallel programming, Parallel Programming with Microsoft Visual C++.

design-patterns parallel parallel-programming patterns ppl

cpp-taskflow - Fast C++ Parallel Programming with Task Dependency Graphs

•    C++

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A fast C++ header-only library to help you quickly build parallel programs with complex task dependencies. Cpp-Taskflow lets you quickly build parallel dependency graphs using modern C++17. It supports both static and dynamic tasking, and is by far faster, more expressive, and easier for drop-in integration than existing libraries.

taskflow task-based-programming cpp17 parallel-programming threadpool concurrent-programming header-only flowgraph high-performance-computing multicore-programming multi-threading taskparallelism multithreading

Pydusa- Parallel Programming in Python

•    C

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Pydusa is a package for parallel programming using Python. It contains a module for doing MPI programming in Python. We have added parallel solver packages such as Parallel SuperLU for solving sparse linear systems.

Concurrent Programming Library

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Concurrent Programming Library provides an opportunity to develop a parallel programs using .net framework 2.0 and above. It includes an implementation of various parallel algorithms, thread-safe collections and patterns.

parallel parallel-programming

MPJ Express - Parallel Programming in Java

•    Java

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MPJ Express is an open source Java message passing library that allows application developers to write and execute parallel applications for multicore processors and compute clusters/clouds. It allows writing parallel Java applications using an MPI-like API.

parallel-programming parallel high-performance-computing hpc

Practical Parallel and Concurrent Programming

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Eight two-week units of courseware (slides, lecture notes, samples, tools) for teaching how to program parallel/concurrent applications at a high-level using Microsoft’s Parallel Extensions to the .NET Framework.

parallel-programming concurrency concurrent performance

future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone

•    R

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The purpose of the future package is to provide a very simple and uniform way of evaluating R expressions asynchronously using various resources available to the user. In programming, a future is an abstraction for a value that may be available at some point in the future. The state of a future can either be unresolved or resolved. As soon as it is resolved, the value is available instantaneously. If the value is queried while the future is still unresolved, the current process is blocked until the future is resolved. It is possible to check whether a future is resolved or not without blocking. Exactly how and when futures are resolved depends on what strategy is used to evaluate them. For instance, a future can be resolved using a sequential strategy, which means it is resolved in the current R session. Other strategies may be to resolve futures asynchronously, for instance, by evaluating expressions in parallel on the current machine or concurrently on a compute cluster.

r cran parallel-processing parallel-computing distributed-computing hpc-clusters hpc promises futures asynchronous programming parallelization

Parallel Runtime Library

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Parallel Runtime Library is optimized library that provide Easy to use and High Performance Parallelism Computing. Parallel Runtime Library provide: Effective Parallel Runtime, Concurrent Data Structure, Task and Data Parallel, Producer and Consumer and Agent Model.

parallel-programming

ArrayFire - Parallel Computing Library

•    C++

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ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming simple. ArrayFire's multiple backends (CUDA, OpenCL and native CPU) make it platform independent and highly portable. A few lines of code in ArrayFire can replace dozens of lines of parallel computing code, saving you valuable time and lowering development costs.

parallel-computing parallel cuda library

Vc - SIMD Vector Classes for C++

•    C++

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Recent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores. Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.

vectorization parallel simd-vector simd-instructions simd avx c-plus-plus avx512 sse neon cpp portable cpp11 cpp14 cpp17 avx2 simd-programming data-parallel parallel-computing

GPdotNET - artificial intelligence tool

•    DotNet

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GPdotNET is artificial intelligence tool for applying Genetic Programming and Genetic Algorithm in modeling and optimization of various engineering problems.

genetic-algorithms genetic-programming parallel parallel-programming

futhark - :boom::computer::boom: A data-parallel functional programming language

•    Haskell

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Futhark is a purely functional data-parallel programming language. Its optimising compiler is able to compile it to typically very performant GPU code. The language and compiler is developed at DIKU at the University of Copenhagen, originally as part of the HIPERFIT centre. Although still under heavy development, Futhark is already useful for practical high-performance programming. For more information, see the website.

language boom gpu hpc compiler gpgpu

.NET Engine for Parallel Multitasked Applications.

•    DotNet

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Nepma can control execution of parallel or sequential tasks using multithreaded approach. It can group tasks and insert pauses between them according to parameter defined by the developer. It has been initially designed to automate redondant tasks originaly executed by human h...

threading engin-for-parallel engine parallel-programming

MPAPI - Parallel and Distributed Applications Framework

•    DotNet

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Message Passing API (MPAPI) is a framework that enables programmers to easily write parallel as well as distributed software systems without having to use standard thread synchronization techniques like locks, monitors, semaphors, mutexes and volatile memory. It is written in...

parallel-programming parallel api asynchronous

StratoSphere - Cloud Computing Framework for Big Data Analytics

•    Java

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The Stratosphere System is an open-source cluster/cloud computing framework for Big Data analytics. It comprises of An extensible higher level language (Meteor) to quickly compose queries for common and recurring use cases, A parallel programming model (PACT, an extension of MapReduce) to run user-defined operations, An efficient massively parallel runtime (Nephele) for fault tolerant execution of acyclic data flows.

cloud-framework cloud big-data parallel information-management

WebMonkeys - Massively parallel GPU programming on JavaScript, simple and clean.

•    Javascript

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Allows you to spawn thousands of parallel tasks on the GPU with the simplest, dumbest API possible. It works on the browser (with browserify) and on Node.js. It is ES5-compatible and doesn't require any WebGL extension. set/get allow you to send/receive data from the GPU, and work creates a number of parallel tasks (monkeys) that can read, process and rewrite that data. The language used is GLSL 1.0, extended array access (foo(index), usable anywhere on the source), setters (foo(index) := value, usable on the end only), and int i, a global variable with the index of the monkey.

webgl parallel primates

Parallel Programming Model

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This project is currently an experiment to offer a parallel programming environment that utilizes a set of networked computers to run user applications using remote pthread and object/memory management. 1st Phase is Linux and C/C++ environment only

C++ AMP: Accelerated Massive Parallelism with Microsoft Visual C++

•    C++

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Samples for the latest Microsoft Press book on programming with C++AMP using Visual Studio 2012.

amp gpgpu parallel parallel-programming visual-studio

FastFlow: programming multi-core

•    C

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Pattern-based multi/many-core parallel programming framework