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Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.

julia julia-language programming-language scientific-computing high-performance-computing numerical-computation machine-learningNNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs. NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

neural-network neural-networks convolutional-layers inference high-performance high-performance-computing simd cpu multithreading fast-fourier-transform winograd-transform matrix-multiplicationA 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 multithreadingThe University of California holds the copyright on all BOINC source code. By submitting contributions to the BOINC code, you irrevocably assign all right, title, and interest, including copyright and all copyright rights, in such contributions to The Regents of the University of California, who may then use the code for any purpose that it desires. BOINC is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

boinc distributed-computing volunteer-computing high-performance-computing citizen-science scientific-computing grid-computing science high-throughput-computing c-plus-plusMPJ 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 hpcA high performance linear algebra library, written in JavaScript and optimized with C++ bindings to BLAS. The documentation is located in the wiki section of this repository.

blas matrix vector linear-algebra high-performance-computing machine-learning linear algebraNeanderthal is a Clojure library for fast matrix and linear algebra computations based on the highly optimized native libraries of BLAS and LAPACK computation routines for both CPU and GPU.. Read the documentation at Neanderthal Web Site.

clojure-library matrix gpu gpu-computing gpgpu opencl cuda high-performance-computing vectorization api matrix-factorization matrix-multiplication matrix-functions matrix-calculationsArraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autogradA Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

bayesian-inference bayesian-data-analysis gpu-computing gpu-acceleration statistics machine-learning clojure-library bayesian opencl cuda high-performance-computing gpu mcmc markov-chain-monte-carloThe drake R package is a workflow manager and computational engine for data science projects. Its primary objective is to keep results up to date with the underlying code and data. When it runs a project, drake detects any pre-existing output and refreshes the pieces that are outdated or missing. Not every runthrough starts from scratch, and the final answers are reproducible. With a user-friendly R-focused interface, comprehensive documentation, and extensive implicit parallel computing support, drake surpasses the analogous functionality in similar tools such as Make, remake, memoise, and knitr. The R community emphasizes reproducibility. Traditional themes include scientific replicability, literate programming with knitr, and version control with git. But internal consistency is important too. Reproducibility carries the promise that your output matches the code and data you say you used.

reproducibility high-performance-computing r data-science drake makefile pipeline workflow reproducible-researchBulk does away with unnecessary boilerplate code and the unsafe API's that are found in for example MPI, or the BSPlib standard. It provides a unified syntax for parallel programming across different platforms and modalities. Our BSP interface supports and encourages the use of modern C++ features, enabling safer and more efficient distributed programming. We have a flexible backend architecture, so that programs written with Bulk work for both shared memory, distributed memory, or mixed systems. Distributed variables are the easiest way to communicate.

parallel-computing parallel-algorithm distributed-computing high-performance-computingThis repository has Snakefiles for common RNA-seq data analysis workflows. Please feel free to copy them and modify them to suit your needs. If you are new to Snakemake, you might like to start by walking through my tutorial for beginners. Next, have a look at Johannes Koster's introductory slides, tutorial, documentation, and FAQ.

bioinformatics rna-seq snakemake lsf-jobs high-performance-computingAs a successor of the packages BatchJobs and BatchExperiments, batchtools provides a parallel implementation of Map for high performance computing systems managed by schedulers like Slurm, Sun Grid Engine, OpenLava, TORQUE/OpenPBS, Load Sharing Facility (LSF) or Docker Swarm (see the setup section in the vignette). Next, you need to setup batchtools for your HPC (it will run sequentially otherwise). See the vignette for instructions.

cran batchjobs batchexperiments slurm lsf sge docker-swarm torque openlava parallel-computing r high-performance-computing reproducibility hpc hpc-clustersComputations are done entirely on the network and without any temporary files on network-mounted storage, so there is no strain on the file system apart from starting up R once per job. This way, we can also send data and results around a lot quicker. All calculations are load-balanced, i.e. workers that get their jobs done faster will also receive more function calls to work on. This is especially useful if not all calls return after the same time, or one worker has a high load.

high-performance-computing cluster r-package slurm sge lsf ssheHive is a system for running computation pipelines on distributed computing resources - clusters, farms or grids. The name comes from the way pipelines are processed by a swarm of autonomous agents.

ensembl workflow-management-system mysql sqlite postgresql high-performance-computing lsf sge htcondor pbs-pro pipeline farm pbspro ehive docker docker-swarmMirror of MFEM - a lightweight, general, scalable C++ library for finite element methods. Please use the official repository, https://github.com/mfem/mfem, to create issues and pull requests. See also the MFEM website:

finite-elements high-order high-performance-computing parallel-computing amr fem computational-science scientific-computing hpcASPECT is a code to simulate convection in Earth's mantle and elsewhere. It has grown from a pure mantle-convection code into a tool for many geodynamic applications including applications for inner core convection, lithospheric scale deformation, two-phase flow, and numerical methods development. The project is supported by CIG (http://geodynamics.org). The steps to install the necessary dependencies and ASPECT itself are described in the Installation instructions section of the ASPECT manual. If you encounter problems during the installation, please consult our wiki for typical installation problems or specific instructions for MacOS users, before asking your question on the mailing list.

c-plus-plus geoscience mantle-convection cig geodynamics high-performance-computingAxiSEM is a parallel spectral-element method to solve 3D wave propagation in a sphere with axisymmetric or spherically symmetric visco-elastic, acoustic, anisotropic structures.

seismology spectral-elements mpi high-performance-computingAfter checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

cuda high-performance-computing gpu-computing gpu-accelerationGaussianProcesses.jl — Gaussian processes (the method) and Simple Kriging are essentially two names for the same concept. The derivation of Kriging estimators, however; does not require distributional assumptions. It is a beautiful coincidence that for multivariate Gaussian distributions, Simple Kriging gives the conditional expectation. Matheron and other important geostatisticians have generalized Gaussian processes to more general random fields with locally-varying mean and for situations where the mean is unknown. GeoStats.jl includes Gaussian processes as a special case as well as other more practical Kriging variants, see the Gaussian processes example. MLKernels.jl — Spatial structure can be represented in many different forms: covariance, variogram, correlogram, etc. Variograms are more general than covariance kernels according to the intrinsically stationary property. This means that there are variogram models with no covariance counterpart. Furthermore, empirical variograms can be easily estimated from the data (in various directions) with an efficient procedure. GeoStats.jl treats variograms as first-class objects, see the Variogram modeling example.

geostatistics kriging statistics variogram spatial-data gaussian-processes covariance interpolation stochastic-processes stochastic-simulation spatial-statistics estimation multiple-point-statistics high-performance-computing
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