132 repository-uri
Utilities that monitor and measure system execution to pinpoint performance issues and analyze computational complexity.
Explore 132 awesome GitHub repositories matching testing & quality assurance · Performance Diagnostics. Refine with filters or upvote what's useful.
React este o bibliotecă JavaScript pentru construirea de interfețe utilizator bazată pe o arhitectură orientată pe componente și flux de date unidirecțional.
Provides high-resolution timing data for component trees to identify performance bottlenecks.
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.
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.
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.
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.
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.
Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ
Measures the quality of generated translations between language pairs using standardized evaluation scripts.
k6 is a developer-centric load testing suite and command-line load generator designed for network performance validation. It functions as a JavaScript load testing tool that utilizes a Go-based runtime engine to simulate concurrent user traffic and validate API responses across HTTP, gRPC, and WebSockets. The project distinguishes itself by using code rather than a graphical interface to define workload scenarios and performance thresholds. It features a pluggable protocol architecture and an extension ecosystem that allows for the addition of custom protocols and specialized testing capabili
Exports raw performance data and granular summary statistics to external tools for deep analysis and visualization.
k6 is a performance testing framework used to measure the scalability and stability of network services and APIs. It functions as a JavaScript load testing tool that uses a Go engine to simulate concurrent user traffic. The tool enables the enforcement of service level objectives by comparing response time percentiles against quantitative performance thresholds. It also operates as a performance regression tool for continuous integration pipelines and a browser performance testing tool that executes scripts within a bundled headless browser instance. Its capabilities cover workload scenario
Captures and filters performance metrics after a run to export data for study or integration with external tools.
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
Evaluates model throughput and latency by simulating concurrent request traffic.
This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
Tracks and displays execution duration for individual code cells to monitor performance.
Hyperfine is a command-line benchmarking tool used to measure the execution time of shell commands through multiple runs and statistical analysis. It functions as a comparative benchmarking utility and a shell performance analyzer, allowing for the evaluation of multiple commands against a reference baseline to determine relative speed. The tool distinguishes itself by isolating actual command performance through shell overhead correction and the ability to bypass the shell entirely using system calls. It supports parameterized execution, enabling benchmarks to run across a range of varying i
Supports high-resolution timing from microseconds to seconds and minimizes noise by bypassing intermediate shells.
Hashcat is a high-performance hash cracking software and OpenCL compute application used to recover plain-text passwords from hashed data. It functions as a GPU-accelerated recovery tool and distributed password cracker, leveraging CPUs and GPUs to perform intensive cryptographic computations. The system differentiates itself through a distributed cracking workflow that coordinates tasks across multiple machines via an overlay network to share computational load. It further optimizes recovery speed using Markov chain keyspace optimization to prioritize the most likely password candidates. Th
Includes tools for measuring the computational speed of hardware specifically for hashing algorithms.
This project is a comprehensive, community-maintained knowledge base and toolkit designed for competitive programming. It serves as a centralized repository for algorithmic theory, data structures, and mathematical techniques, providing a structured reference for informatics and collegiate programming competitions. The project distinguishes itself by integrating educational content with a robust suite of automation utilities. It provides a complete workflow for competitive programming, including tools for automated test case generation, solution verification, and direct interaction with onlin
Tracks the duration of program execution to analyze performance and resource usage during development or testing.
LibGDX is a Java-based framework designed for cross-platform game development, enabling the creation and deployment of 2D and 3D games across desktop, mobile, and web environments from a single codebase. It functions as a comprehensive library that abstracts hardware-accelerated graphics, audio, input, and file system access, providing a unified interface for developers to manage game logic and application lifecycles. The framework distinguishes itself through a high-performance architecture that prioritizes efficiency and native interoperability. It utilizes a batch-oriented graphics pipelin
Provides performance measurement interfaces for tracking execution time and resource load of code blocks.
This project is an educational platform and tutorial series designed to teach the Go programming language through the practice of test-driven development. It provides a structured path for developers to master language fundamentals, concurrency, and standard library usage by building functional applications in small, verifiable increments. The core methodology centers on the test-driven development cycle, where failing tests are written before implementation to define requirements and ensure code correctness. This approach is applied across a wide range of practical scenarios, including the c
Provides tools and techniques to measure code performance and identify bottlenecks through repeated execution.
FastMCP is a Python framework designed for building servers that expose functions, resources, and prompts to AI models using the Model Context Protocol. It simplifies the development process by automatically deriving tool metadata, input schemas, and documentation directly from Python function signatures and type hints. The framework provides a unified container for managing these components, allowing developers to build modular applications that integrate seamlessly with AI assistants. The project distinguishes itself through its support for interactive, server-defined user interface compone
Measures and logs the duration of tool calls, resource reads, and prompt retrievals to identify performance bottlenecks.
promptfoo is an evaluation framework for measuring the performance of large language model prompts, agents, and retrieval augmented generation pipelines. It provides a suite of tools for conducting comparative benchmarking and executing automated quality and security regressions. The system features a benchmarking suite for running identical prompts across different model providers to compare output quality side-by-side. It also includes a dedicated red teaming tool for identifying security vulnerabilities and prompt injection risks through automated penetration testing. The framework suppor
Generates shareable reports and visualizations of performance data for team reviews.
This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha
Calculates operation completion times to isolate performance bottlenecks in read and write paths.
Laravel Debugbar is a web-based debugging toolbar and application profiler for Laravel. It provides a visual interface to inspect database queries, logs, and performance metrics in real time to identify and resolve bugs during development. The tool features a database query monitor to capture SQL statements and timings, as well as a request inspector for analyzing route metadata, loaded views, and HTTP request data. It includes a profiler for measuring execution time and memory usage to identify bottlenecks in the request lifecycle. Its observability capabilities cover exception capture, app
Measures execution time and dumps variables inside templates to optimize rendering performance.