Showing 397 open source projects for "source code computer"

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  • 1
    NonlinearSolve.jl

    NonlinearSolve.jl

    High-performance and differentiation-enabled nonlinear solvers

    Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia. The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative...
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  • 2
    ParallelStencil.jl

    ParallelStencil.jl

    Package for writing high-level code for parallel stencil computations

    ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array...
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  • 3
    CxxWrap

    CxxWrap

    Package to make C++ libraries available in Julia

    This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a...
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  • 4
    Apache Seata

    Apache Seata

    High-performance, open source distributed transaction solution

    Seata is a distributed transaction solution for microservices that provides consistent, cross-service commits without forcing every team to adopt the same persistence model. Its architecture separates responsibilities into a global coordinator and per-service participants, so business services remain decoupled while transactions are orchestrated centrally. Multiple modes are supported—AT (automatic, SQL-based with undo logs), TCC (try-confirm-cancel), Saga (long-running compensation), and...
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  • 5
    gtsummary

    gtsummary

    Presentation-Ready Data Summary and Analytic Result Tables

    gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting.
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  • 6
    JUDI.jl

    JUDI.jl

    Julia Devito inversion

    JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling...
    Downloads: 1 This Week
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  • 7
    SDGym

    SDGym

    Benchmarking synthetic data generation methods

    The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the...
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  • 8
    geemap

    geemap

    A Python package for interactive geospaital analysis and visualization

    A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE...
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  • 9
    NVIDIA Merlin

    NVIDIA Merlin

    Library providing end-to-end GPU-accelerated recommender systems

    ...Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code.
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  • 10
    Arrow Julia

    Arrow Julia

    Official Julia implementation of Apache Arrow

    This is a pure Julia implementation of the Apache Arrow data standard. This package provides Julia AbstractVector objects for referencing data that conforms to the Arrow standard. This allows users to seamlessly interface Arrow formatted data with a great deal of existing Julia code.
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  • 11
    FastGaussQuadrature.jl

    FastGaussQuadrature.jl

    Julia package for Gaussian quadrature

    A Julia package to compute n-point Gauss quadrature nodes and weights to 16-digit accuracy and in O(n) time. So far the package includes gausschebyshev(), gausslegendre(), gaussjacobi(), gaussradau(), gausslobatto(), gausslaguerre(), and gausshermite(). This package is heavily influenced by Chebfun.
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  • 12
    Gradle Docker Compose Plugin

    Gradle Docker Compose Plugin

    Simplifies usage of Docker Compose for integration testing

    The Gradle Docker Compose Plugin by Avast integrates Docker Compose lifecycle management into Gradle builds. It allows developers to define and manage Docker containers required for integration testing or local development directly from their Gradle build scripts. This plugin automates the startup and shutdown of services, supports container health checks, and enables tight integration between application code and containerized services, enhancing reproducibility and automation in...
    Downloads: 1 This Week
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  • 13
    Ridgepole

    Ridgepole

    Ridgepole is a tool to manage DB schema. It defines DB schema

    Ridgepole is a database schema management tool that treats your schema as code by expressing it in a Ruby DSL and applying diffs to keep databases in sync. You describe the desired state in a “Schemafile” (tables, columns, indexes, constraints), and Ridgepole compares it with the live database to generate only the necessary changes. This diff-and-apply approach makes schema changes repeatable and reviewable, avoiding hand-written migrations for routine structural edits. It supports multiple...
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  • 14
    Tidier.jl

    Tidier.jl

    Meta-package for data analysis in Julia, modeled after the R tidyverse

    Tidier.jl is a Julia package that brings tidyverse-style data manipulation and analysis to Julia, inspired by R's dplyr and tidyverse. It allows users to write expressive and concise data transformation code using chaining (|>) and intuitive syntax. Built on top of DataFrames.jl, Tidier.jl aims to make data wrangling more accessible to users familiar with R or looking for cleaner data pipelines in Julia.
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  • 15
    Bumper.jl

    Bumper.jl

    Bring Your Own Stack

    Bumper.jl is a package that aims to make working with bump allocators (also known as arena allocators) easier and safer. You can dynamically allocate memory to these bump allocators, and reset them at the end of a code block, just like Julia's stack. Allocating to a bump allocator with Bumper.jl can be just as efficient as stack allocation. Bumper.jl is still a young package, and may have bugs. Let me know if you find any.
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  • 16
    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.
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  • 17
    LLVM.jl

    LLVM.jl

    Julia wrapper for the LLVM C API

    A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language.
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  • 18
    DearPyGui

    DearPyGui

    Graphical User Interface Toolkit for Python with minimal dependencies

    Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers. DPG is well suited for creating simple user interfaces as well as developing complex...
    Downloads: 2 This Week
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  • 19
    Dagster

    Dagster

    An orchestration platform for the development, production

    Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust...
    Downloads: 3 This Week
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  • 20
    Scio

    Scio

    A Scala API for Apache Beam and Google Cloud Dataflow

    Scio is a Scala API developed by Spotify that builds on Apache Beam to enable expressive batch and streaming data pipelines, optimized for running on Google Cloud Dataflow. Inspired by Spark and Scalding, it provides scalable, type‑safe, and production-grade data processing, with built-in support for BigQuery, Pub/Sub, Cassandra, Elasticsearch, Redis, TensorFlow IO, and more.
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  • 21
    sparklyr

    sparklyr

    R interface for Apache Spark

    sparklyr is an R package that provides seamless interfacing with Apache Spark clusters—either local or remote—while letting users write code in familiar R paradigms. It supplies a dplyr-compatible backend, Spark machine learning pipelines, SQL integration, and I/O utilities to manipulate and analyze large datasets distributed across cluster environments.
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  • 22
    Dask

    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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  • 23
    QuantumClifford.jl

    QuantumClifford.jl

    Clifford circuits, graph states, and other quantum Stabilizer tools

    A Julia package for working with quantum stabilizer states and Clifford circuits that act on them. Graphs states are also supported. The package is already very fast for the majority of common operations, but there are still many low-hanging fruits performance-wise. See the detailed suggested readings & references page for background on the various algorithms.
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  • 24
    Enzyme.jl

    Enzyme.jl

    Julia bindings for the Enzyme automatic differentiator

    This is a package containing the Julia bindings for Enzyme. This is very much a work in progress and bug reports/discussion is greatly appreciated. Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM. It is highly-efficient and its ability perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools.
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  • 25
    POCO

    POCO

    Cross-platform C++ libraries for building network applications

    The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++...
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