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4 dépôts

Awesome GitHub RepositoriesTile-Based Kernel Authoring

Implementation of memory-tiling strategies within GPU kernels to optimize data movement and thread synchronization.

Distinct from GPU Kernel Implementations: Distinct from general GPU kernel implementation by focusing specifically on the tile programming model for memory efficiency.

Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Tile-Based Kernel Authoring. Refine with filters or upvote what's useful.

Awesome Tile-Based Kernel Authoring GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • dusty-nv/jetson-inferenceAvatar de dusty-nv

    dusty-nv/jetson-inference

    8,734Voir sur GitHub↗

    jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

    Implements a tile programming model in C++ and Python to manage high-performance data movement across GPU threads.

    C++caffecomputer-visiondeep-learning
    Voir sur GitHub↗8,734
  • nvidia/isaac-gr00tAvatar de NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Voir sur GitHub↗

    Uses a block-based programming model where threads cooperate on data tiles to leverage Tensor Cores for matrix multiplication and Fourier transforms.

    Jupyter Notebook
    Voir sur GitHub↗6,222
  • tile-ai/tilelangAvatar de tile-ai

    tile-ai/tilelang

    5,226Voir sur GitHub↗

    TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc

    Provides a tile-level programming model that abstracts individual thread management.

    Python
    Voir sur GitHub↗5,226
  • xtensor-stack/xtensorAvatar de xtensor-stack

    xtensor-stack/xtensor

    3,748Voir sur GitHub↗

    xtensor is a C++ multidimensional array library for numerical computing that provides N-dimensional containers with an interface mirroring the NumPy API. It utilizes a lazy evaluation expression engine to defer numerical computations until assignment, which minimizes memory allocations and intermediate copies. The library features a foreign memory array adaptor that allows it to wrap external buffers, such as NumPy arrays, to perform numerical operations in-place without duplicating data. It further optimizes performance through lazy broadcasting and a system that manages the lifetime of temp

    Calculates standard elementary math functions, including trigonometric and exponential operations, element-wise.

    C++c-plus-plus-14multidimensional-arraysnumpy
    Voir sur GitHub↗3,748
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  2. Artificial Intelligence & ML
  3. GPU Kernel Implementations
  4. Tile-Based Kernel Authoring

Explorer les sous-tags

  • Elementwise Tile Math Operations1 sous-tagApplying standard math functions elementwise on tile fragments in GPU kernels. **Distinct from Tile-Based Kernel Authoring:** Distinct from Tile-Based Kernel Authoring: focuses on elementwise math operations on tiles, not general tile programming.
  • GEMV Shared Memory TilersWrites GEMV kernels that tile the vector and matrix into shared memory for efficient data reuse. **Distinct from Tile-Based Kernel Authoring:** Distinct from Tile-Based Kernel Authoring: focuses on GEMV-specific tiling, not general tile-based kernel authoring.
  • Python Tile Kernel InterfacesProvides Python APIs to express and execute tile-based parallel programs using the CUDA Tile programming model. **Distinct from Tile-Based Kernel Authoring:** Distinct from Tile-Based Kernel Authoring: focuses on Python-level APIs for tile programming, not low-level CUDA implementation.
  • Tile-Based GPU Kernel ProgrammingUses a block-based programming model where threads cooperate on data tiles to leverage Tensor Cores for matrix multiplication and Fourier transforms. **Distinct from Tile-Based Kernel Authoring:** Distinct from Tile-Based Kernel Authoring: focuses on the programming model for tile-based GPU kernels, not just memory-tiling strategies.