Open Source OCaml (Objective Caml) Data Management Systems

OCaml (Objective Caml) Data Management Systems

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Browse free open source OCaml (Objective Caml) Data Management Systems and projects below. Use the toggles on the left to filter open source OCaml (Objective Caml) Data Management Systems by OS, license, language, programming language, and project status.

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  • 1
    CamlOSG is an OCaml bindings for OpenSceneGraph.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    This library provides an easy interface to Gnuplot (http://www.gnuplot.info/) from OCaml (http://caml.inria.fr/) for static 2D and 3D graphs and simple animations. This library aims to be portable on all platforms OCaml runs on.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 3
    DataKit

    DataKit

    Connect processes into powerful data pipelines

    Connect processes into powerful data pipelines with a simple git-like filesystem interface. DataKit is a tool to orchestrate applications using a Git-like dataflow. It revisits the UNIX pipeline concept, with a modern twist: streams of tree-structured data instead of raw text. DataKit allows you to define complex build pipelines over version-controlled data. DataKit is currently used as the coordination layer for HyperKit, the hypervisor component of Docker for Mac and Windows, and for the DataKitCI continuous integration system. src contains the main DataKit service. This is a Git-like database to which other services can connect. ci contains DataKitCI, a continuous integration system that uses DataKit to monitor repositories and store build results. The easiest way to use DataKit is to start both the server and the client in containers.
    Downloads: 0 This Week
    Last Update:
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  • 4

    ParTools

    Support for manual parallelization of sequential C programs.

    ParTools allows the interactive analysis of a C program execution profile and data dependencies to facilitate the discovery and selection of suitable parallelization candidates in a manual parallelization process. The flow does not assume any specific parallelization technique, thus it can be broadly applied. The original (serial) C source is automatically annotated to trace the execution profile and data dependencies at run-time. The annotated program is then executed using a significant (but small) data set selected by the developer. The data collected is cross-referenced with the original source and can be interactively analyzed graphically to determine the best parallelization candidates and techniques.
    Downloads: 0 This Week
    Last Update:
    See Project
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