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293 个仓库

Awesome GitHub RepositoriesPerformance Testing and Analysis

Tools for benchmarking, profiling, and diagnosing system responsiveness and throughput.

Explore 293 awesome GitHub repositories matching testing & quality assurance · Performance Testing and Analysis. Refine with filters or upvote what's useful.

Awesome Performance Testing and Analysis GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • sindresorhus/awesomesindresorhus 的头像

    sindresorhus/awesome

    476,211在 GitHub 上查看↗

    这是一个由社区维护的目录,作为软件工具、框架和教育资源的综合索引。它充当开源知识库,将不同的工程领域和技术资源组织成结构化的分类体系,以帮助开发者发现高质量内容。 该目录通过去中心化的同行评审模型脱颖而出,由独立贡献者策划、验证和更新条目,以确保准确性和相关性。所有信息均以版本控制的纯文本 Markdown 格式存储,确保了整个集合的平台独立性、透明度和可审计性。 该项目涵盖了广泛的能力领域,包括技术资源发现、职业发展和软件开发知识管理。它提供结构化的学习路径、基础设施和安全工具、数据管理实用程序,以及从医疗保健到数字人文等领域的专业资源。 该仓库作为公共版本控制集合进行维护,支持程序化访问和社区驱动的数据更新。

    Simulates concurrent user traffic to evaluate application responsiveness and stability under load.

    awesomeawesome-listlists
    在 GitHub 上查看↗476,211
  • donnemartin/system-design-primerdonnemartin 的头像

    donnemartin/system-design-primer

    353,387在 GitHub 上查看↗

    这是一个关于分布式系统架构和后端基础设施设计的综合教育资源和学习指南。它为掌握设计复杂软件系统所需的扩展性、可靠性和性能原则提供了结构化课程。 该仓库通过提供一种系统化的技术面试准备方法脱颖而出,结合了设计模式、架构权衡和间隔重复工具,帮助用户记忆复杂概念。它强调约束驱动的分析,教授用户在起草架构设计时如何评估延迟、一致性和可用性等相互竞争的需求。 内容涵盖了广泛的系统设计能力,包括数据库扩展、流量管理和基础设施优化策略。它详细介绍了水平扩展、多层缓存、异步通信和服务发现技术,同时还提供了用于执行资源估算和容量规划的框架。 文档以学习指南的形式组织,为后端工程和大规模系统设计的基础知识提供了系统化的路径。

    Covers techniques for subjecting software to high-volume traffic to evaluate stability and responsiveness under load.

    Pythondesigndesign-patternsdesign-system
    在 GitHub 上查看↗353,387
  • facebook/reactfacebook 的头像

    facebook/react

    245,669在 GitHub 上查看↗

    React 是一个用于构建用户界面的 JavaScript 库,采用组件化架构和单向数据流。

    Provides high-resolution timing data for component trees to identify performance bottlenecks.

    JavaScriptjavascriptuifrontend
    在 GitHub 上查看↗245,669
  • nodejs/nodenodejs 的头像

    nodejs/node

    117,932在 GitHub 上查看↗

    This project is an open-source JavaScript runtime built on the V8 engine. It provides a comprehensive environment for executing JavaScript code outside of a web browser, offering foundational primitives for process management, multi-core load distribution, and parallel execution through worker threads. The runtime includes a broad set of built-in modules for system-level operations, such as file system interaction, network communication across various protocols, and cryptographic security. It supports multiple module systems, native binary addon integration, and diagnostic tools for monitorin

    Captures high-resolution timing data and resource usage metrics to assist in profiling application performance.

    JavaScriptjavascriptjslinux
    在 GitHub 上查看↗117,932
  • pytorch/pytorchpytorch 的头像

    pytorch/pytorch

    100,814在 GitHub 上查看↗

    PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

    Visualizes hardware utilization, operator latency, and memory metrics to provide a comprehensive view of runtime performance.

    Pythonautograddeep-learninggpu
    在 GitHub 上查看↗100,814
  • macrozheng/mallmacrozheng 的头像

    macrozheng/mall

    83,878在 GitHub 上查看↗

    This project is an enterprise-grade Java framework designed for building scalable, full-stack e-commerce applications. It provides a comprehensive foundation for microservice-based distributed architectures, enabling the development of complex retail platforms that include product management, order processing, and secure user authentication. By leveraging modular service patterns and centralized API gateways, the framework supports the construction of resilient systems that decompose monolithic business logic into independent, manageable services. The platform distinguishes itself through a r

    Traces method execution and monitors resource consumption to identify performance bottlenecks.

    Javadockerelasticsearchelk
    在 GitHub 上查看↗83,878
  • thedotmack/claude-memthedotmack 的头像

    thedotmack/claude-mem

    82,698在 GitHub 上查看↗

    Claude-mem is an agentic memory persistence system designed to provide AI assistants with long-term context across multiple development sessions. It functions as a background orchestrator that captures, summarizes, and indexes interaction history, allowing models to maintain continuity and recall technical decisions from past tasks. By utilizing a vector-augmented context engine, the system injects relevant historical observations into active sessions, ensuring that AI agents remain informed without exceeding finite token budgets. The project distinguishes itself through an endless memory arc

    Provides tools to compare token usage and tool call frequency across tasks.

    JavaScriptaiai-agentsai-memory
    在 GitHub 上查看↗82,698
  • grafana/grafanagrafana 的头像

    grafana/grafana

    74,456在 GitHub 上查看↗

    Grafana is an observability data platform designed to aggregate metrics, logs, and traces from diverse sources into a unified environment. It functions as a centralized interface for visualizing complex telemetry data, transforming raw streams into interactive dashboards that support real-time system health tracking and performance monitoring. The platform distinguishes itself through a plugin-based modular architecture that integrates disparate databases, cloud services, and monitoring tools via a standardized data abstraction layer. This framework allows for the dynamic loading of external

    Facilitates deep inspection of telemetry data to pinpoint performance bottlenecks and optimize application efficiency.

    TypeScriptalertinganalyticsbusiness-intelligence
    在 GitHub 上查看↗74,456
  • apple/swiftapple 的头像

    apple/swift

    70,119在 GitHub 上查看↗

    Swift is a general purpose, compiled systems programming language designed for building high-performance software. It is a strongly typed language that focuses on memory safety and type safety to prevent runtime errors. The language is designed for native code integration, allowing it to interoperate with C and Objective-C libraries to leverage existing system functions and high-performance APIs. The project covers broad capabilities in type-safe application development and cross-platform toolchain engineering. It includes infrastructure for automated language validation, compiler performanc

    Measures compilation timing and counter data against baselines to detect and prevent performance regressions.

    Swift
    在 GitHub 上查看↗70,119
  • sindresorhus/awesome-nodejssindresorhus 的头像

    sindresorhus/awesome-nodejs

    65,973在 GitHub 上查看↗

    This project is a community-driven directory that aggregates essential software projects and educational content for the Node.js ecosystem. It functions as a centralized knowledge base and discovery index, designed to simplify the navigation of a fragmented technical landscape by providing a structured collection of high-quality links, tools, and learning materials. The repository distinguishes itself through a decentralized, peer-reviewed curation model. By utilizing standard version control workflows and pull requests, the community ensures that all listed resources undergo human verificati

    Collects precise measurement utilities for evaluating code execution speed and memory footprint.

    awesomeawesome-listjavascript
    在 GitHub 上查看↗65,973
  • burntsushi/ripgrepBurntSushi 的头像

    BurntSushi/ripgrep

    65,112在 GitHub 上查看↗

    ripgrep is a command-line utility designed for searching through large file trees and source code repositories. It functions as a recursive text processor that traverses directories to locate and display matching patterns, serving as a high-performance alternative to traditional search tools. The tool distinguishes itself through a focus on execution speed and intelligent file handling. It utilizes a finite automata-based regular expression engine to ensure linear time complexity and employs hardware-level acceleration for literal byte sequence scanning. By integrating with version control sy

    Demonstrates superior search speeds compared to traditional tools, serving as a high-performance alternative for large codebases.

    Rustclicommand-linecommand-line-tool
    在 GitHub 上查看↗65,112
  • kdn251/interviewskdn251 的头像

    kdn251/interviews

    64,941在 GitHub 上查看↗

    This project serves as a centralized knowledge base and study guide for mastering computer science fundamentals and technical interview preparation. It provides a structured collection of algorithmic implementations, data structure guides, and theoretical references designed to support professional development and problem-solving skills. The repository distinguishes itself through a taxonomy-based organization that maps complex concepts into a hierarchical structure. It standardizes the expression of abstract data structures and algorithms using a consistent programming language, with impleme

    Applies standard mathematical notation to quantify the execution time and space requirements of various algorithms.

    Javaalgorithmalgorithm-challengesalgorithm-competitions
    在 GitHub 上查看↗64,941
  • keras-team/keraskeras-team 的头像

    keras-team/keras

    64,094在 GitHub 上查看↗

    Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning

    Quantifies computational efficiency and execution speed across various hardware backends to identify optimal configurations for complex models.

    Pythondata-sciencedeep-learningjax
    在 GitHub 上查看↗64,094
  • addyosmani/agent-skillsaddyosmani 的头像

    addyosmani/agent-skills

    60,849在 GitHub 上查看↗

    Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated

    Provides methodologies for collecting timing and health data using synthetic tools and real user monitoring.

    Shellagent-skillsantigravityantigravity-ide
    在 GitHub 上查看↗60,849
  • deepfakes/faceswapdeepfakes 的头像

    deepfakes/faceswap

    55,289在 GitHub 上查看↗

    Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

    Benchmarks graphics hardware by tracking memory usage and throughput across varying batch sizes to refine pipeline performance.

    Pythondeep-face-swapdeep-learningdeep-neural-networks
    在 GitHub 上查看↗55,289
  • crewaiinc/crewaicrewAIInc 的头像

    crewAIInc/crewAI

    53,687在 GitHub 上查看↗

    CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

    CrewAI evaluates system efficiency by running multiple iterations to generate detailed metrics on task scores and execution times.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗53,687
  • ethereum/go-ethereumethereum 的头像

    ethereum/go-ethereum

    51,178在 GitHub 上查看↗

    Geth is a comprehensive execution client for the Ethereum network, serving as a foundational node implementation that processes transactions, maintains the distributed ledger state, and participates in peer-to-peer consensus. It provides a robust infrastructure for synchronizing, validating, and serving blockchain data, utilizing a persistent Merkle Patricia Trie database to ensure the cryptographic integrity of historical records. As a sandboxed smart contract runtime, it executes bytecode according to deterministic protocol rules, enabling the deployment and interaction of decentralized appl

    Geth provides a no-op tracer to measure execution overhead or verify tracer infrastructure without collecting additional transaction data during the execution process.

    Goblockchainethereumgeth
    在 GitHub 上查看↗51,178
  • jakevdp/pythondatasciencehandbookjakevdp 的头像

    jakevdp/PythonDataScienceHandbook

    48,561在 GitHub 上查看↗

    This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

    Provides timing utilities to measure and evaluate code execution performance.

    Jupyter Notebookjupyter-notebookmatplotlibnumpy
    在 GitHub 上查看↗48,561
  • aider-ai/aiderAider-AI 的头像

    Aider-AI/aider

    46,305在 GitHub 上查看↗

    Aider is a command-line interface tool that enables large language models to directly edit, refactor, and manage source code within a local repository. It functions as an AI-powered coding assistant that integrates into the developer workflow, allowing users to apply code changes through natural language prompts while maintaining repository context and version control. The tool distinguishes itself through a specialized diff-based patching engine that parses model-generated search-and-replace blocks to modify specific file segments without rewriting entire files. It features a provider-agnost

    Measures coding performance by tracking the percentage of tasks completed correctly and adherence to edit formats.

    Pythonanthropicchatgptclaude-3
    在 GitHub 上查看↗46,305
  • grpc/grpcgrpc 的头像

    grpc/grpc

    44,891在 GitHub 上查看↗

    gRPC is a language-agnostic remote procedure call framework designed for high-performance communication between distributed services. It utilizes a structured interface definition language to generate consistent client stubs and server skeletons, enabling applications to invoke methods on remote servers as if they were local objects. By leveraging the HTTP/2 transport layer, the framework supports efficient binary serialization and multiplexed data exchange across diverse programming environments. The framework distinguishes itself through its support for flexible communication patterns, incl

    Executes performance benchmarks using worker processes to calculate latency and throughput metrics.

    C++
    在 GitHub 上查看↗44,891
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探索子标签

  • Benchmarks3 个子标签Standardized datasets and metrics used to evaluate and compare the performance or capabilities of software systems.
  • Game Performance AnalyticsAnalyzing match history, win rates, and deck efficiency to evaluate gameplay performance. **Distinct from Performance Analysis:** Analyzes player/deck success rates rather than software execution performance or algorithmic efficiency.
  • Mechanical Simulation EnvironmentsIntegrated simulation tools for testing the physical performance and structural integrity of mechanical designs. **Distinct from Performance Testing and Analysis:** Distinct from general performance testing; focuses on mechanical engineering simulation.
  • Performance1 个子标签Tools designed to measure and evaluate the speed, responsiveness, and stability of software under various conditions.
  • Performance Analysis12 个子标签Methods and tools for interpreting performance data to identify bottlenecks and evaluate algorithmic efficiency.
  • Performance Diagnostics4 个子标签Utilities that monitor and measure system execution to pinpoint performance issues and analyze computational complexity.
  • Performance Profiling9 个子标签Specialized tools that monitor resource usage and execution time to identify performance bottlenecks within specific hardware or software components.
  • Performance Testing2 个子标签Tools that subject software to high-volume traffic or stress to evaluate stability and responsiveness under load.