5 Repos
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.
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Awesome Shared Memory Arrays GitHub Repositories
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- codebasics/py
codebasics/py
7,262Auf GitHub ansehenThis 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 - nvidia/warp
NVIDIA/warp
6,233Auf GitHub ansehenWarp 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 - nvidia/isaac-gr00t
NVIDIA/Isaac-GR00T
6,222Auf GitHub ansehenProvides a shared-memory programming model with point-to-point, collective, and atomic operations for parallel applications.
Jupyter Notebook - ispc/ispc
ispc/ispc
2,843Auf GitHub ansehenISPC 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 - androidx/media
androidx/media
2,680Auf GitHub ansehenAndroid 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
