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Awesome GitHub RepositoriesLoop Optimizations

Compiler transformations that optimize iteration structures to reduce runtime overhead.

Distinguishing note: None of the candidates describe compiler-level loop transformation; they focus on agent loops, event loops, or infinite loops.

Explore 6 awesome GitHub repositories matching programming languages & runtimes · Loop Optimizations. Refine with filters or upvote what's useful.

Awesome Loop Optimizations GitHub Repositories

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  • federico-busato/modern-cpp-programmingfederico-busato 的头像

    federico-busato/Modern-CPP-Programming

    15,808在 GitHub 上查看↗

    This project is a comprehensive educational resource and programming course covering C++ language semantics and features from C++03 through C++26. It provides structured tutorials and technical guides focused on modern C++ development. The material offers specialized instruction on template metaprogramming, including the use of type traits and compile-time computations. It features detailed guides on concurrency and parallelism for multi-core execution, as well as a reference for software design applying SOLID principles and RAII. Additionally, it covers build performance optimization to redu

    Covers compiler-level loop transformations including fission, fusion, and tiling to improve execution speed.

    HTMLc-plus-pluscode-qualitycompilers
    在 GitHub 上查看↗15,808
  • cython/cythoncython 的头像

    cython/cython

    10,767在 GitHub 上查看↗

    Cython is a compiler that translates Python code into C or C++ to create high-performance extension modules. It functions as a static typing optimizer and a C extension generator, allowing developers to declare C types within Python code to reduce interpreter overhead and increase execution speed. The project enables the wrapping of external C libraries to provide high-level interfaces to low-level system capabilities. It also serves as a native binary packager, capable of freezing scripts and their dependencies into standalone executable binaries for distribution. The system covers a broad

    Transforms integer loops into efficient C implementations to reduce runtime overhead and execution time.

    Cythonbig-dataccpp
    在 GitHub 上查看↗10,767
  • openvinotoolkit/openvinoopenvinotoolkit 的头像

    openvinotoolkit/openvino

    10,414在 GitHub 上查看↗

    OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

    Detects and replaces parameter-result pairs in models containing loops to reduce inference latency.

    C++aicomputer-visiondeep-learning
    在 GitHub 上查看↗10,414
  • databricks/scala-style-guidedatabricks 的头像

    databricks/scala-style-guide

    2,784在 GitHub 上查看↗

    Recommends using while loops over for loops or functional transformations in performance-critical code.

    在 GitHub 上查看↗2,784
  • intel/llvmintel 的头像

    intel/llvm

    1,497在 GitHub 上查看↗

    The project is a reusable collection of modular compiler and toolchain technologies designed for building optimizers, code generators, and multi-language programming environments. It provides foundational compiler frontend technologies that translate source code written in C, C++, and Objective-C into a low-level programming language and intermediate code format. This intermediate representation enables cross-language analysis, program transformation, and target-independent optimization alongside a cross-platform programming framework that allows developers to write single-source accelerated a

    Transforms control flow structures using mathematical models and linear programming to determine the most efficient execution layouts.

    LLVMintelllvmoneapi
    在 GitHub 上查看↗1,497
  • tiramisu-compiler/tiramisuTiramisu-Compiler 的头像

    Tiramisu-Compiler/tiramisu

    960在 GitHub 上查看↗

    Tiramisu is a polyhedral C++ compiler framework designed to express and optimize data-parallel algorithms for diverse hardware accelerators. It provides a programming interface that allows developers to define mathematical expressions, manage loop iteration spaces, and organize functions targeting heterogeneous architectures. The system features an advanced compilation infrastructure that abstracts computations into a hardware-agnostic intermediate representation before lowering them into native machine code or hardware configuration bitstreams. It calculates exact data flow dependencies thro

    Applies mathematical polytope models to reorder loop iterations and optimize data locality across multi-dimensional arrays.

    C++code-generationcompilerdeep-neural-networks
    在 GitHub 上查看↗960
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探索子标签

  • InferenceSpecific compiler and graph transformations that optimize iterative model execution to reduce latency. **Distinct from Loop Optimizations:** Distinct from general loop optimizations by targeting neural network parameter-result pairs in inference graphs.
  • Mathematical OptimizationTransforms compiler control flow structures using mathematical models and linear programming to determine efficient execution layouts. **Distinct from Loop Optimizations:** Distinct from general Loop Optimizations: specifically applies mathematical programming models to restructure iteration spaces.
  • PolyhedralTransforms nested loops and data layouts using the polyhedral model to maximize hardware efficiency and performance. **Distinct from Loop Optimizations:** Distinct from general loop optimizations: transforms nested loops specifically via the polyhedral model for hardware efficiency.
  • Polyhedral Loop Transformation EnginesApplies mathematical polytope models to reorder loop iterations and optimize data locality across multi-dimensional arrays. **Distinct from Loop Optimizations:** Distinct from general loop optimizations: applies mathematical polytope models to reorder loop iterations and optimize data locality.
  • Polyhedral OptimizationsApplies advanced loop transformations and data layout rearrangements based on the polyhedral model to maximize hardware efficiency. **Distinct from Loop Optimizations:** Distinct from general loop optimizations: applies advanced polyhedral model transformations and data layout rearrangements.
  • While Loop PreferencesThe practice of using while loops instead of for loops or functional transformations in performance-critical sections to avoid virtual calls and boxing. **Distinct from Loop Optimizations:** Distinct from Loop Optimizations: focuses on the manual coding preference for while loops in Scala, not compiler-level loop transformations.