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2 Repos

Awesome GitHub RepositoriesNVIDIA GPU Kernels

Optimized mathematical kernels specifically written for NVIDIA GPU architectures.

Distinct from NVIDIA Hardware Acceleration: Candidates focused on video decoding or cloud providers; this is about local runtime kernels via AOTInductor and Triton.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · NVIDIA GPU Kernels. Refine with filters or upvote what's useful.

Awesome NVIDIA GPU Kernels GitHub Repositories

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  • nvidia/deeplearningexamplesAvatar von NVIDIA

    NVIDIA/DeepLearningExamples

    14,819Auf GitHub ansehen↗

    This project is a collection of optimized scripts, deployment patterns, and reference implementations designed for scaling and accelerating state-of-the-art AI models. It serves as a multi-domain model zoo and a distributed training framework, providing PyTorch reference implementations for training and deploying models on GPU-accelerated infrastructure. The repository distinguishes itself through an optimization suite focused on NVIDIA GPU hardware, utilizing automatic mixed precision and specialized math modes to increase training speed and throughput. It provides enterprise deployment patt

    Provides reference patterns for accelerating training speed through mixed precision and specialized math modes on NVIDIA hardware.

    Jupyter Notebookcomputer-visiondeep-learningdrug-discovery
    Auf GitHub ansehen↗14,819
  • iree-org/ireeAvatar von iree-org

    iree-org/iree

    3,819Auf GitHub ansehen↗

    IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis

    Compiles models to target specific Nvidia GPU architectures using a dedicated CUDA backend.

    C++compilercudajax
    Auf GitHub ansehen↗3,819
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Unter-Tags erkunden

  • CUDA Backend CompilationCompilation of model operations into optimized kernels for NVIDIA GPU architectures. **Distinct from NVIDIA GPU Kernels:** Focuses on the compiler backend process for NVIDIA GPUs rather than just the kernels themselves
  • Optimization PatternsStandardized implementation patterns for maximizing hardware utilization and training speed on specific GPU architectures. **Distinct from NVIDIA GPU Kernels:** Focuses on high-level reference patterns and configurations rather than the low-level kernel code itself.