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3 个仓库

Awesome GitHub RepositoriesAlgorithm Performance Benchmarking

Tools for measuring and comparing the execution performance of different algorithmic implementations.

Distinct from Attribution Algorithm Benchmarks: Existing candidates are too specific to AI interpretability or formula evaluators.

Explore 3 awesome GitHub repositories matching testing & quality assurance · Algorithm Performance Benchmarking. Refine with filters or upvote what's useful.

Awesome Algorithm Performance Benchmarking GitHub Repositories

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  • omkarpathak/pygorithmOmkarPathak 的头像

    OmkarPathak/pygorithm

    4,385在 GitHub 上查看↗

    Pygorithm 是一个 Python 算法库和计算逻辑参考。它提供了一系列标准算法实现,以及用于分析特定函数运行时效率和扩展性能的工具。 该项目支持检查内部函数的底层源代码,以研究逻辑是如何构建和执行的。它将特定的实现映射到其理论上的时间复杂度和空间复杂度,以进行性能分析。 该库被组织成离散的模块,支持在可变数据集上执行标准算法模式。它包括用于对 Python 实现进行基准测试和计算渐近复杂度的功能。

    Provides capabilities for benchmarking Python implementations to evaluate the performance of different computational approaches.

    Python
    在 GitHub 上查看↗4,385
  • open-quantum-safe/liboqsopen-quantum-safe 的头像

    open-quantum-safe/liboqs

    2,979在 GitHub 上查看↗

    Liboqs is a C library that provides a unified interface for quantum-resistant cryptographic algorithms, including key encapsulation and digital signature mechanisms. It is designed to facilitate the integration of post-quantum security into existing protocols and applications, ensuring data protection against future threats from large-scale quantum computers. The library distinguishes itself through a focus on high-assurance implementations and side-channel resistance, utilizing constant-time primitives to prevent timing-based information leakage. It offers extensive build-time configuration,

    Evaluates the execution speed and efficiency of security algorithms using high-precision hardware counters.

    Ccryptographykey-exchange-algorithmslattice-based-crypto
    在 GitHub 上查看↗2,979
  • vincentqyw/image-matching-webuiVincentqyw 的头像

    Vincentqyw/image-matching-webui

    1,283在 GitHub 上查看↗

    This project is a web-based platform designed for benchmarking, visualizing, and evaluating computer vision algorithms focused on image feature extraction and matching. It provides a unified interface to compare the performance and accuracy of different models by processing image pairs or live video streams. The system distinguishes itself through a modular architecture that allows users to define custom processing pipelines and register external algorithms via configuration files. It incorporates geometric verification techniques to refine visual data and improve the precision of detected co

    Evaluates the accuracy and speed of different feature extraction and matching models through a visual benchmarking interface.

    Pythonaspanformerdeep-learningfeature-matching
    在 GitHub 上查看↗1,283
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