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2 Repos

Awesome GitHub RepositoriesZero-Copy Buffer Interoperability

Mechanisms for sharing GPU memory buffers between different libraries without duplicating data to system memory.

Distinct from GPU Acceleration Libraries: Focuses on the high-performance sharing of memory buffers between libraries, rather than general GPU offloading or library integration.

Explore 2 awesome GitHub repositories matching devops & infrastructure · Zero-Copy Buffer Interoperability. Refine with filters or upvote what's useful.

Awesome Zero-Copy Buffer Interoperability GitHub Repositories

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  • software-mansion/typegpuAvatar von software-mansion

    software-mansion/TypeGPU

    2,564Auf GitHub ansehen↗

    TypeGPU is a tool for type-safe WebGPU development that enables writing shaders in TypeScript. It translates high-level TypeScript function definitions and structures into WebGPU Shading Language source code to automate shader generation and validate logic using a type system. The project provides a mechanism for cross-library GPU interoperability by sharing typed buffers without copying data to system memory. It also integrates the Model Context Protocol to allow AI agents to inspect generated shader code and diagnose runtime errors. The system manages WebGPU resource mapping through typed

    Provides a mechanism for sharing typed GPU buffers between libraries without copying data to system memory.

    TypeScriptgpgpugpugpu-computing
    Auf GitHub ansehen↗2,564
  • google-ai-edge/litertAvatar von google-ai-edge

    google-ai-edge/LiteRT

    2,561Auf GitHub ansehen↗

    LiteRT is a runtime and API for executing machine learning and generative AI models on mobile, desktop, and IoT hardware. It consists of an inference engine and a specialized environment for running quantized large language and diffusion models locally on edge hardware. The system includes an ahead-of-time model compiler that translates models into hardware-specific bytecode to reduce startup latency and memory overhead. It provides a unified interface for Neural Processing Units with automatic fallback routing to CPUs or GPUs when specific subgraph support is unavailable. An edge model conve

    Passes tensor data directly to accelerators without duplicating data to system memory to reduce latency and power.

    C++
    Auf GitHub ansehen↗2,561
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