Samply is a cross-platform CPU sampling profiler and performance analysis utility. It consists of a command-line tool for recording process stack traces at regular intervals and a visual interface for analyzing the resulting execution data. The system includes a debug symbol resolver that maps raw memory addresses to human-readable function names using local or remote symbol information. It transforms recorded execution data into flame graphs and timelines to pinpoint function-level hotspots. The tool provides capabilities for CPU execution recording, stack unwinding, and symbol resolution a
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
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
vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks. The project distinguishes itself through a suite of visual diagnostics, including flame graphs for stack visualization and heatmaps that map execution frequency and duration directly onto source code. It also includes a remote performance monitor capable of capturing function-specific metrics from a running server and streaming
go-torch est un outil de profilage pour capturer l'état d'exécution des programmes Go et transformer les données binaires brutes en représentations visuelles de la performance du programme. Il fonctionne comme un profileur de flame graph et un utilitaire de visualisation de performance qui identifie les chemins de code coûteux via la collecte de traces de pile CPU et mémoire.
Les fonctionnalités principales de uber-archive/go-torch sont : Flame Graph Visualizers, CPU Profilers, Remote Profiling Streams, Synthesis Workflows, Call Stack Samplers, Remote Profiling Interfaces, Application Stack Trace Capturers, Go Runtime Profilers.
Les alternatives open-source à uber-archive/go-torch incluent : mstange/samply — Samply is a cross-platform CPU sampling profiler and performance analysis utility. It consists of a command-line tool… nswbmw/node-in-debugging — This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and… google/gops — gops is a command-line diagnostic toolset for monitoring, profiling, and managing the runtime state of active Go… nvdv/vprof — vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption.… jvm-profiling-tools/async-profiler — Async-profiler is a suite of performance tools designed for sampling Java runtimes, tracking memory allocations, and… koute/bytehound — Bytehound is a Linux memory profiler that utilizes a custom global allocator to intercept memory requests and track…