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