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 este un mediu de dezvoltare integrat vizual și un motor de jocuri pentru crearea de jocuri pe 8 biți pentru hardware-ul Game Boy. Funcționează ca un compilator ROM pentru hardware retro care transpila logica grafică în assembly Z80 nativ și imagini binare compatibile cu handheld-urile și emulatoarele originale. Proiectul servește drept instrument de build cross-platform, generând atât ROM-uri hardware native, cât și build-uri compatibile cu web dintr-o singură sursă de proiect. Utilizează o interfață drag-and-drop pentru logica jocului și designul scenelor, permițând crearea de mecanici de joc și plasarea activelor fără a scrie cod mașină de nivel scăzut. Setul de instrumente include un pipeline pentru procesarea imaginilor și audio-ului brut în formate și palete specifice hardware-ului. De asemenea, oferă capabilități de profilare a performanței pentru a analiza statisticile de sincronizare și logurile de execuție pentru optimizarea responsivității software-ului pe hardware limitat.

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 este o suită de instrumente de performanță concepute pentru eșantionarea runtime-urilor Java, urmărirea alocărilor de memorie și monitorizarea contoarelor hardware. Funcționează ca un profiler de eșantionare cu overhead redus pentru aplicațiile Java, colectând stack trace-uri și date de alocare a memoriei fără bias de safepoint. Proiectul oferă utilitare specializate pentru generarea de flame graph-uri interactive pentru a vizualiza hotspot-urile de execuție într-un browser web. Include un monitor de contor de performanță hardware pentru a urmări evenimentele de sistem de nivel scăzut, cum ar fi cache misses și page faults. Setul de instrumente acoperă mai multe domenii de diagnosticare, inclusiv profilarea utilizării CPU pentru a identifica metodele hot, urmărirea alocării memoriei pentru heap și leak-uri native, și analiza contenciei thread-urilor pentru a descoperi blocajele de sincronizare între diferite straturi ale sistemului.

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 is a command-line diagnostic toolset for monitoring, profiling, and managing the runtime state of active Go applications. It functions as a runtime diagnostic tool that provides a focused interface for analyzing memory, profiling performance, and monitoring the health of running processes. The tool provides a set of specialized utilities including a performance profiler for capturing CPU and heap profiles, a memory analyzer for identifying leaks and triggering garbage collection, and a process monitor for discovering running binaries and visualizing process hierarchies. The project cove

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 este un framework de machine learning și toolkit de arhitectură de rețele neuronale utilizat pentru construirea, antrenarea și implementarea modelelor. Funcționează ca un strat de abstractizare hardware, mapând grafuri computaționale de nivel înalt către instrucțiuni de nivel scăzut pe diverse backend-uri CPU și GPU și sisteme de operare. Sistemul permite designul unor structuri flexibile de modele printr-o arhitectură modulară unde straturile container reutilizabile încapsulează ponderi și operații matematice. Acest lucru permite compunerea rețelelor neuronale complexe prin componente imbricate. Framework-ul include un pipeline de inginerie a datelor pentru transformarea seturilor de date brute în tensori curați și un profiler de performanță computațională pentru a identifica blocajele de runtime folosind instrumentație de diagnosticare. Aceste capabilități susțin optimizarea calculului de înaltă performanță și implementarea modelelor cross-hardware.

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