4 repository-uri
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.
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 este un optimizator CSS pentru Node.js care funcționează ca minifier, bundler și post-procesor. Este conceput pentru a reduce volumul total al fișierelor de stil prin eliminarea spațiilor albe, a comentariilor și a codului redundant. Proiectul oferă un pipeline pentru aplicarea transformărilor personalizate și a ajustărilor de compatibilitate cu browserele. Permite modificarea programatică a regulilor și valorilor CSS printr-un sistem de plugin-uri și utilizarea plugin-urilor de optimizare personalizate. Instrumentul acoperă o gamă largă de capabilități de optimizare a activelor, inclusiv gruparea fișierelor de stil, înlănțuirea regulilor de import și rebasarea URL-urilor relative. De asemenea, suportă generarea de source map-uri pentru depanare și formatarea output-ului personalizabilă pentru înfrumusețare.
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.