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Showing 7 open source projects for "loop-aes"

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  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

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    Waitlist App and Paging System

    We make it cheaper and easier to manage your waitlist, order backlog, and just about any other waiting scenario.

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  • 1
    Finch.jl

    Finch.jl

    Sparse tensors in Julia and more

    Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code.
    Downloads: 3 This Week
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  • 2
    OhMyREPL.jl

    OhMyREPL.jl

    Syntax highlighting and other enhancements for the Julia REPL

    OhMyREPL.jl is a Julia package that enhances the Julia REPL (Read-Eval-Print Loop) experience with syntax highlighting, bracket matching, prompt customization, and automatic indentation. It is designed to make the command-line interface more visually appealing and user-friendly, especially during interactive development and debugging. It runs entirely in the terminal and does not require external dependencies or GUI.
    Downloads: 4 This Week
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  • 3
    Distances.jl

    Distances.jl

    A Julia package for evaluating distances (metrics) between vectors

    A Julia package for evaluating distances (metrics) between vectors. This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation.
    Downloads: 0 This Week
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  • 4
    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: 1 This Week
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  • Eurekos LMS - Build a Smarter Customer Icon
    Eurekos LMS - Build a Smarter Customer

    The Eurekos customer training LMS makes it easy to deliver product training that retains more customers and transforms partners into advocates.

    Eurekos is a purpose-built LMS that engages customers throughout the entire learning journey from pre-sales, to onboarding, and everything after.
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  • 5
    Gridap.jl

    Gridap.jl

    Grid-based approximation of partial differential equations in Julia

    Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes. It also provides methods for time integration. Gridap is extensible and modular. One can...
    Downloads: 2 This Week
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  • 6
    Transducers.jl

    Transducers.jl

    Efficient transducers for Julia

    ...Furthermore, transducers can be defined without specifying the details of the input and output (collections, streams, channels, etc.) and therefore achieves a full reusability. Transducers are introduced by Rich Hickey, the creator of the Clojure language. His Strange Loop talk is a great introduction to the idea of transducers. Transducers.jl is an implementation of the transducers in Julia. Aiming to satisfy the high-performance needs of Julia users, Transducers.jl uses a formulation that is pure and aiding type-stability.
    Downloads: 0 This Week
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  • 7
    FLoops.jl

    FLoops.jl

    Fast sequential, threaded, and distributed for-loops for Julia

    Fast sequential, threaded, and distributed for-loops for Julia, fold for humans.FLoops.jl provides a macro @floop. It can be used to generate a fast generic sequential and parallel iteration over complex collections. Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provides a simple distributed executor.
    Downloads: 0 This Week
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