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6 repositorios

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

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • pytorch/captumAvatar de pytorch

    pytorch/captum

    5,652Ver en GitHub↗

    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
    Ver en GitHub↗5,652
  • pytoolz/toolzAvatar de pytoolz

    pytoolz/toolz

    5,117Ver en GitHub↗

    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
    Ver en GitHub↗5,117
  • acceleratehs/accelerateAvatar de AccelerateHS

    AccelerateHS/accelerate

    1,012Ver en GitHub↗

    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
    Ver en GitHub↗1,012
  • amplab-extras/sparkr-pkgAvatar de amplab-extras

    amplab-extras/SparkR-pkg

    642Ver en GitHub↗

    R frontend for Spark

    Frontend for Apache Spark.

    R
    Ver en GitHub↗642
  • vertica/distributedrAvatar de vertica

    vertica/DistributedR

    162Ver en GitHub↗

    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
    Ver en GitHub↗162
  • vertica/ddrAvatar de vertica

    vertica/ddR

    119Ver en GitHub↗

    Standard API for Distributed Data Structures in R

    Distributed data structures.

    R
    Ver en GitHub↗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.