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6 Repos

Awesome GitHub RepositoriesParallel Computing

Frameworks for distributed and parallel processing.

Explore 6 awesome GitHub repositories matching part of an awesome list · Parallel Computing. Refine with filters or upvote what's useful.

Awesome Parallel Computing GitHub Repositories

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  • pytorch/captumAvatar von pytorch

    pytorch/captum

    5,652Auf GitHub ansehen↗

    Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

    Distributes attribution algorithm calculations across multiple processors or GPUs to speed up processing.

    Python
    Auf GitHub ansehen↗5,652
  • pytoolz/toolzAvatar von pytoolz

    pytoolz/toolz

    5,117Auf GitHub ansehen↗

    Toolz is a Python library that implements functional programming utilities for iterable transformation, dictionary manipulation, function composition, and lazy evaluation. It provides a set of pure functions designed to work with Python's built-in data structures, enabling concise and composable data processing workflows. What distinguishes toolz is its support for curried partial application, allowing functions to be incrementally applied and reused. It includes dictionary-centric operations that handle nested structures, and offers iterable chain transformers that combine mapping, filtering

    Executes functions across multiple cores by serializing input data and aggregating results.

    Python
    Auf GitHub ansehen↗5,117
  • acceleratehs/accelerateAvatar von AccelerateHS

    AccelerateHS/accelerate

    1,012Auf GitHub ansehen↗

    Accelerate is a framework for high-performance array computing that provides a domain-specific language for expressing complex mathematical and parallel computations. By utilizing a declarative programming interface, it allows users to define high-level array transformations that are automatically translated into optimized machine code for diverse hardware architectures. The system distinguishes itself through a modular architecture that decouples high-level array operations from hardware-specific instructions. It employs just-in-time compilation and kernel fusion to transform programs into e

    Executes collective operations on multi-dimensional arrays across multicore processors and graphics processing units.

    Haskellacceleratecudagpu
    Auf GitHub ansehen↗1,012
  • amplab-extras/sparkr-pkgAvatar von amplab-extras

    amplab-extras/SparkR-pkg

    642Auf GitHub ansehen↗

    R frontend for Spark

    Frontend for Apache Spark.

    R
    Auf GitHub ansehen↗642
  • vertica/distributedrAvatar von vertica

    vertica/DistributedR

    162Auf GitHub ansehen↗

    Distributed R is a scalable high-performance platform for the R language. It enables and accelerates large scale machine learning, statistical analysis, and graph processing.

    Scalable high-performance platform.

    R
    Auf GitHub ansehen↗162
  • vertica/ddrAvatar von vertica

    vertica/ddR

    119Auf GitHub ansehen↗

    Standard API for Distributed Data Structures in R

    Distributed data structures.

    R
    Auf GitHub ansehen↗119
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  • Attribution DistributionDistributes attribution algorithm calculations across multiple processors or GPUs to speed up processing. **Distinct from Parallel Computing:** Distinct from general Parallel Computing: specifically distributes attribution algorithm calculations, not general-purpose parallel processing.