4 مستودعات
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 هو محسن CSS لـ Node.js يعمل كمصغر، ومجمع، ومعالج لاحق. تم تصميمه لتقليل إجمالي حجم أوراق الأنماط عن طريق إزالة المسافات البيضاء، والتعليقات، والكود الزائد. يوفر المشروع خط أنابيب لتطبيق تحويلات مخصصة وتعديلات توافق المتصفح. يسمح بالتعديل البرمجي لقواعد وقيم CSS من خلال نظام مكون إضافي واستخدام مكونات تحسين مخصصة. تغطي الأداة مجموعة واسعة من إمكانيات تحسين الأصول، بما في ذلك تجميع أوراق الأنماط، وتضمين قاعدة الاستيراد، وإعادة تعيين رابط URL النسبي. كما يدعم توليد خرائط المصدر لتصحيح الأخطاء وتنسيق المخرجات القابل للتخصيص للتجميل.
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.