Showing 6 open source projects for "parallel computing"

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  • 1
    Dagger.jl

    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.
    Downloads: 6 This Week
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  • 2
    101-0250-00

    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.
    Downloads: 0 This Week
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  • 3
    FileTrees.jl

    FileTrees.jl

    Parallel file processing made easy

    ...When computing lazy trees, these values are held in distributed memory and operated on in parallel.
    Downloads: 0 This Week
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  • 4
    The Julia Programming Language

    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. ...
    Downloads: 9 This Week
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  • 5
    LoopVectorization.jl

    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.
    Downloads: 3 This Week
    Last Update:
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  • 6
    ModelingToolkit.jl

    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.
    Downloads: 1 This Week
    Last Update:
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