Search Results for "parallel computing"
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Showing 209 open source projects for "parallel computing"
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Parallel Octave
Parallel computing in GNU Octave
The library consists of three main functions: a) parstart - this is an asynchronous function call b) running - to check the current status of the started function c) get_data - to get returned parameters from the called function In contrast to the other Octave Parallel package (https://wiki.octave.org/Parallel_package), this tool actually makes a common asynchronous programming in Octave possible just like in Python or C#. -
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FreeRDP
FreeRDP is a free remote desktop protocol library and clients
FreeRDP is a free implementation of the Remote Desktop Protocol (RDP), released under the Apache license. Enjoy the freedom of using your software wherever you want, the way you want it, in a world where interoperability can finally liberate your computing experience. The nightly builds are installed into /opt/freerdp-nightly and can be installed in parallel with the distributions regular freerdp package. Network tracing of RDP can be quite a challenge due to a number of factors... -
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HDF5
Official HDF5® Library Repository
HDF5 (Hierarchical Data Format v5) is a widely-used data management library and file format for storing large and complex scientific data sets efficiently. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
..., embedded, or automotive product platforms. TensorRT is built on CUDA®, NVIDIA’s parallel programming model, and enables you to optimize inference leveraging libraries, development tools, and technologies in CUDA-X™ for artificial intelligence, autonomous machines, high-performance computing, and graphics. With new NVIDIA Ampere Architecture GPUs, TensorRT also leverages sparse tensor cores providing an additional performance boost. -
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LWJGL
Java library that enables cross-platform access to popular native APIs
LWJGL is a Java library that enables cross-platform access to popular native APIs useful in the development of graphics (OpenGL, Vulkan), audio (OpenAL) and parallel computing (OpenCL) applications. This access is direct and high-performance, yet also wrapped in a type-safe and user-friendly layer, appropriate for the Java ecosystem. LWJGL is an enabling technology and provides low-level access. It is not a framework and does not provide higher-level utilities than what the native libraries... -
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis... -
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Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. Automatic symbolic transformations, such as index... -
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Sogou C++ Workflow
C++ parallel computing and asynchronous networking engine
As Sogou`s C++ server engine, Sogou C++ Workflow supports almost all back-end C++ online services of Sogou, including all search services, cloud input method, online advertisements, etc., handling more than 10 billion requests every day. This is an enterprise-level programming engine in light and elegant design which can satisfy most C++ back-end development requirements. -
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ROOT
Analyzing, storing and visualizing big data, scientifically
ROOT is a unified software package for the storage, processing, and analysis of scientific data: from its acquisition to the final visualization in the form of highly customizable, publication-ready plots. It is reliable, performant and well supported, easy to use and obtain, and strives to maximize the quantity and impact of scientific results obtained per unit cost, both of human effort and computing resources. ROOT provides a very efficient storage system for data models, that demonstrated... -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing... -
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LoopVectorization.jl
Macro(s) for vectorizing loops
LoopVectorization.jl is a Julia package for accelerating numerical loops by automatically applying SIMD (Single Instruction, Multiple Data) vectorization and other low-level optimizations. It analyzes loops and generates highly efficient code that leverages CPU vector instructions, making it ideal for performance-critical computing in fields such as scientific computing, signal processing, and machine learning. -
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101-0250-00
ETH course - Solving PDEs in parallel on GPUs
This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering. -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without... -
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FileTrees.jl
Parallel file processing made easy
... is running with. Files and subtrees in a file tree can have any value attached to them, you can map and reduce over these values, or combine them by merging or collapsing trees or subtrees. When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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go-streams
A lightweight stream processing library for Go
A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. -
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ComputeSharp
.NET library to run C# code in parallel on the GPU through DX12
ComputeSharp is a .NET library to run C# code in parallel on the GPU through DX12 and dynamically generated HLSL compute shaders. The available APIs let you access GPU devices, allocate GPU buffers and textures, move data between them and the RAM, write compute shaders entirely in C# and have them run on the GPU. The goal of this project is to make GPU computing easy to use for all .NET developers! You can use this library to create all sorts of things from scientific simulations, to animated... -
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LinDB
LinDB is a scalable, high performance, high availability database
LinDB is a scalable, high-performance, high-availability distributed time series database. A single server could easily support more than one million write TPS; With fundamental techniques like efficient compression storage and parallel computing, LinDB delivers highly optimized query performance. The multi-channel replication protocol supports any amount of nodes, and ensures the system's availability. Schema-free multi-dimensional data model with Metric, Tags, and Fields; The LinQL... -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if you're... -
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Octave Forge
A collection of packages providing extra functionality for GNU Octave
Octave Forge is a central location for collaborative development of packages for GNU Octave. The Octave Forge packages expand Octave's core functionality by providing field specific features via Octave's package system. See https://octave.sourceforge.io/packages.php for a list of all available packages. GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and... -
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OpenFOAM
The Open Source CFD Toolbox
OpenFOAM - The Open Source CFD Toolbox. OpenFOAM is a generic, programmable software tool for Computational Fluid Dynamics (CFD). For more information, see: - Main Website: https://openfoam.org - C++ Source Documentation: https://cpp.openfoam.org - Issue (Bug) Tracking: https://bugs.openfoam.org - User Guide: https://cfd.direct/openfoam/user-guide -
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