awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

4 dépôts

Awesome GitHub RepositoriesGPU Kernel Optimization Levels

Selecting compiler optimization levels specifically for GPU kernel code to balance compile time and execution speed.

Distinct from Compiler Optimizations: Distinct from general Compiler Optimizations: focuses on per-kernel optimization level selection for GPU code, not CPU binary optimization.

Explore 4 awesome GitHub repositories matching software engineering & architecture · GPU Kernel Optimization Levels. Refine with filters or upvote what's useful.

Awesome GPU Kernel Optimization Levels GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • nvidia/warpAvatar de NVIDIA

    NVIDIA/warp

    6,233Voir sur 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

    Selects the optimization level applied to GPU kernels, trading compile time for execution speed.

    Pythoncudadifferentiable-programminggpu
    Voir sur GitHub↗6,233
  • nvidia/isaac-gr00tAvatar de NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Voir sur GitHub↗

    Applies link-time optimization to select the best GPU kernels for a given configuration without manual tuning.

    Jupyter Notebook
    Voir sur GitHub↗6,222
  • clean-css/clean-cssAvatar de clean-css

    clean-css/clean-css

    4,201Voir sur GitHub↗

    Clean-CSS est un optimiseur CSS Node.js qui fonctionne comme un minificateur, un bundler et un post-processeur. Il est conçu pour diminuer le volume total des feuilles de style en supprimant les espaces blancs, les commentaires et le code redondant. Le projet fournit un pipeline pour appliquer des transformations personnalisées et des ajustements de compatibilité des navigateurs. Il permet la modification programmatique des règles et valeurs CSS via un système de plugin et l'utilisation de plugins d'optimisation personnalisés. L'outil couvre un large éventail de capacités d'optimisation d'actifs, incluant le regroupement de feuilles de style, l'intégration des règles d'importation et le rebasage des URL relatives. Il prend également en charge la génération de source maps pour le débogage et le formatage de sortie personnalisable pour l'embellissement.

    Provides selectable optimization levels to control the aggressiveness of CSS code reduction.

    JavaScript
    Voir sur GitHub↗4,201
  • iree-org/ireeAvatar de iree-org

    iree-org/iree

    3,819Voir sur GitHub↗

    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

    Adjusts LLVM optimization levels for generated code to isolate bugs or identify race conditions.

    C++compilercudajax
    Voir sur GitHub↗3,819
  1. Home
  2. Software Engineering & Architecture
  3. Compiler Optimizations
  4. GPU Kernel Optimization Levels

Explorer les sous-tags

  • Automatic SelectionAutomatically selects the best-performing GPU kernel for a given operation and problem size using link-time optimization. **Distinct from GPU Kernel Optimization Levels:** Distinct from GPU Kernel Optimization Levels: focuses on automatic selection via link-time optimization, not manual level tuning.
  • CSS Optimization LevelsConfigurable levels of aggressive code reduction to balance output file size and structural safety. **Distinct from GPU Kernel Optimization Levels:** Distinct from GPU Kernel Optimization Levels by focusing on stylesheet minification rather than GPU binary execution speed.