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5 repositorios

Awesome GitHub RepositoriesShared Memory Arrays

Concurrent data structures allocated in shared memory for inter-process communication.

Distinct from Shared Memory Configuration Stores: Closest candidates are too specific to config stores or node graphs; this covers general shared memory arrays for parallelism.

Explore 5 awesome GitHub repositories matching operating systems & systems programming · Shared Memory Arrays. Refine with filters or upvote what's useful.

Awesome Shared Memory Arrays GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • codebasics/pyAvatar de codebasics

    codebasics/py

    7,262Ver en GitHub↗

    This project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning. The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization. The curriculum covers broader capability area

    Provides implementation examples for using shared memory arrays to share state across concurrent Python processes.

    Jupyter Notebookjupyterjupyter-notebookjupyter-notebooks
    Ver en GitHub↗7,262
  • nvidia/warpAvatar de NVIDIA

    NVIDIA/warp

    6,233Ver en GitHub↗

    Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

    Shares GPU array data with CuPy and Numba via the CUDA array interface protocol.

    Pythoncudadifferentiable-programminggpu
    Ver en GitHub↗6,233
  • nvidia/isaac-gr00tAvatar de NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Ver en GitHub↗

    Provides a shared-memory programming model with point-to-point, collective, and atomic operations for parallel applications.

    Jupyter Notebook
    Ver en GitHub↗6,222
  • ispc/ispcAvatar de ispc

    ispc/ispc

    2,843Ver en GitHub↗

    ISPC is a vectorizing compiler and SIMD parallel programming language that implements a single program multiple data model. It serves as a toolchain for translating C-based code with parallel extensions into optimized machine code for various CPU and GPU architectures using an LLVM backend. The compiler is designed for cross-platform SIMD toolchain support, generating specialized instruction sets for x86 SSE/AVX, ARM NEON, and Intel GPU from a single source. It features a runtime dispatch mechanism that selects the most efficient hardware-specific implementation for the current system during

    Provides a programming model with parallel extensions to run many program instances simultaneously on vector hardware.

    C++compilerintelispc
    Ver en GitHub↗2,843
  • androidx/mediaAvatar de androidx

    androidx/media

    2,680Ver en GitHub↗

    Android Media is a framework library providing the primary system components for audio and video playback, session management, and media routing on Android. It includes a multimedia API for processing raw media streams, managing MIDI devices, and rendering video frames. The project features a dedicated metadata manager for organizing descriptive labels, content channels, and DRM configurations, alongside a session controller that synchronizes playback state with external controllers and manages media shortcuts for wearable devices. The library covers a broad range of capabilities including a

    Moves raw media data between processes using shared memory mapped files to avoid expensive copying.

    Javaandroidexoplayerjava
    Ver en GitHub↗2,680
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  • Programming Models1 sub-etiquetaProvides a shared-memory programming model with point-to-point, collective, and atomic operations for parallel applications. **Distinct from Shared Memory Arrays:** Distinct from Shared Memory Arrays: covers the programming model abstraction (point-to-point, collectives, atomics) rather than the concrete data structures allocated in shared memory.