Awesome GitHub RepositoriesExecution Profilers

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

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

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

Blender is a professional 3D creation suite designed for modeling, animation, rendering, and video editing. It functions as an open-source 3D engine that provides a comprehensive framework for procedural geometry, physics simulation, and high-quality visual output. The platform is built upon a foundational architecture that utilizes data-block-based memory management and a dependency-graph-based evaluation system to handle complex scene transformations and geometry updates. The software distinguishes itself through a highly modular, node-based procedural architecture that allows users to cons

Vitest is a high-performance testing framework designed for JavaScript and TypeScript applications. It provides an integrated environment that supports unit, integration, and browser-based testing, allowing developers to execute test suites natively without requiring separate build steps or complex configuration. The project distinguishes itself through a highly optimized execution model that leverages worker-thread isolation and on-demand module transformation to provide rapid feedback. It includes a comprehensive suite of mocking and spying utilities that allow for the interception of depen

py-spy is a sampling profiler and process debugger for Python. It allows for the analysis of running processes to identify performance bottlenecks and diagnose hanging programs without requiring code changes or restarts. The tool operates by reading the memory of a running process from the outside, which enables non-invasive sampling and state collection without pausing execution. It can resolve binary symbols to capture performance data from native extensions written in compiled languages and generate visual flame graphs for both native extensions and subprocesses. The project provides capa

Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

Firefox is a cross-platform web browser engine designed to render web content, execute JavaScript, and manage secure browsing sessions. It utilizes a multi-process isolation architecture that distributes browser tasks across independent operating system processes to ensure stability and prevent site-specific failures from impacting the entire application. The engine incorporates a sandboxed execution environment to restrict web content and untrusted scripts to isolated memory compartments, enforcing security policies that prevent unauthorized access to system resources. The project distinguis

MAME is a vintage hardware emulation platform designed to recreate the circuitry of arcade games, computers, and consoles to run original software on modern devices. It functions as a retro gaming preservation framework for managing, verifying, and archiving ROM sets and disk images to ensure long-term software accessibility. The project features a system debugging tool for inspecting emulated memory, CPU registers, and execution flow via breakpoints and disassembly. It also includes a Lua-based automation layer that exposes core system state and hardware controls for custom behavior and anal

GB Studio es un entorno de desarrollo integrado visual y motor de juegos para crear juegos de 8 bits para hardware Game Boy. Funciona como un compilador de ROM de hardware retro que transpila lógica gráfica en ensamblador Z80 nativo e imágenes binarias compatibles con dispositivos portátiles originales y emuladores. El proyecto sirve como una herramienta de construcción multiplataforma, generando tanto ROMs de hardware nativo como builds compatibles con web desde una única fuente de proyecto. Utiliza una interfaz de arrastrar y soltar para la lógica del juego y el diseño de escenas, permitiendo la creación de mecánicas de juego y colocación de activos sin escribir código de máquina de bajo nivel. El conjunto de herramientas incluye un pipeline para procesar imágenes y audio sin procesar en formatos y paletas específicos de hardware. También proporciona capacidades de perfilado de rendimiento para analizar estadísticas de tiempo y registros de ejecución para optimizar la capacidad de respuesta del software en hardware limitado.

pprof is a tool for visualizing and analyzing performance profiling data. It converts sampled call stacks into a directed graph rendered as an SVG, enabling visual identification of execution hotspots. The tool also parses Linux perf.data files, converting them into an internal profile representation for further analysis. Beyond visualization, pprof provides a command-line REPL for interactive exploration of profiling data, allowing users to filter, refine, and query performance information on the fly. It generates sorted text reports that highlight the most resource-intensive call stacks, an

Async-profiler es un conjunto de herramientas de rendimiento diseñadas para muestrear runtimes de Java, rastrear asignaciones de memoria y monitorear contadores de hardware. Funciona como un perfilador de muestreo de baja sobrecarga para aplicaciones Java, recopilando trazas de pila y datos de asignación de memoria sin sesgo de safepoint. El proyecto proporciona utilidades especializadas para generar gráficos de llama interactivos para visualizar puntos calientes de ejecución en un navegador web. Incluye un monitor de contador de rendimiento de hardware para rastrear eventos del sistema de bajo nivel, como fallos de caché y fallos de página. El conjunto de herramientas cubre varios dominios de diagnóstico, incluyendo el perfilado de uso de CPU para identificar métodos calientes, seguimiento de asignación de memoria para fugas de heap y nativas, y análisis de contención de hilos para descubrir cuellos de botella de sincronización a través de diferentes capas del sistema.

Async-profiler is a sampling profiler for Java applications that tracks CPU time and stack traces across execution frames to identify performance bottlenecks. It is designed to capture profiling data without introducing timing bias. The project provides capabilities for JVM memory analysis to locate native and heap allocation hotspots and memory leaks. It also includes system contention analysis to identify resource bottlenecks through the tracking of contended locks and hardware performance counters. The tool converts raw profiling data into visual performance reports, including interactive

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

gops es un conjunto de herramientas de diagnóstico de línea de comandos para monitorear, perfilar y gestionar el estado de ejecución de aplicaciones Go activas. Funciona como una herramienta de diagnóstico en tiempo de ejecución que proporciona una interfaz enfocada para analizar la memoria, perfilar el rendimiento y monitorear la salud de los procesos en ejecución. La herramienta proporciona un conjunto de utilidades especializadas, incluido un perfilador de rendimiento para capturar perfiles de CPU y heap, un analizador de memoria para identificar fugas y activar la recolección de basura, y un monitor de procesos para descubrir binarios en ejecución y visualizar jerarquías de procesos. El proyecto cubre una amplia gama de capacidades de diagnóstico, incluido el rastreo de ejecución en tiempo de ejecución, la captura de trazas de pila y el muestreo de uso de recursos. También incluye utilidades del sistema para la inspección de metadatos de procesos, resolución de rutas binarias y el monitoreo de métricas de tiempo de ejecución como conteos de hilos y estadísticas de memoria.

size-limit is a set of specialized tools for measuring JavaScript bundle sizes and enforcing performance budgets within continuous integration pipelines. It functions as a bundle size monitor and budget enforcer that can reject pull requests when JavaScript bundles exceed predefined size thresholds. The project distinguishes itself by providing a browser-based execution profiler that calculates the time required to compile and execute JavaScript on simulated low-end hardware. It also includes a tree-shaking validator that analyzes partial import bundle sizes to verify that unused code is corr

This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Leaf is a machine learning framework and neural network architecture toolkit used for building, training, and deploying models. It functions as a hardware abstraction layer, mapping high-level computational graphs to low-level instructions across various CPU and GPU backends and operating systems. The system enables the design of flexible model structures through a modular architecture where reusable container layers encapsulate weights and mathematical operations. This allows for the composition of complex neural networks via nested components. The framework includes a data engineering pipe

This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr