awesome-repositories.com
Blog
MCP
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
uber-archive avatar

uber-archive/go-torchArchived

0
View on GitHub↗
3,941 stars·235 forks·Go·MIT·12 vues

Go Torch

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.

L'outil dispose d'un profileur de processus distant capable de réseau qui se connecte aux endpoints pour capturer et exporter des profils d'exécution à partir de binaires Go. Il utilise le profilage stochastique pour synthétiser les données d'exécution en graphes d'appel, permettant l'identification des goulots d'étranglement dans les applications en cours d'exécution.

Le projet couvre le pipeline complet de collecte de données de profilage, de l'échantillonnage de trace de pile et l'acquisition de profil distant à la synthèse de graphe d'appel. Ces flux de travail aboutissent à la génération de flame graphs basés sur SVG qui visualisent la profondeur des fonctions et la fréquence d'exécution.

Features

  • Flame Graph Visualizers - Generates SVG-based flame graphs from processed call graph data to visualize function depth and execution frequency.
  • CPU Profilers - Captures CPU and memory usage data from remote endpoints or local binaries to generate raw call graphs.
  • Remote Profiling Streams - Retrieves binary execution data over a network connection to analyze processes running on separate host machines.
  • Synthesis Workflows - Transforms raw stack trace samples into a hierarchical tree structure representing program execution flow.
  • Call Stack Samplers - Captures periodic snapshots of the call stack to statistically estimate resource usage without recording every function call.
  • Remote Profiling Interfaces - Connects to remote endpoints to capture and export execution profiles from Go binaries over a network.
  • Application Stack Trace Capturers - Captures the execution state of running programs to identify where time is spent and locate bottlenecks.
  • Go Runtime Profilers - Provides a profiler that reads Go runtime profiling data to generate call graphs and flame graphs.
  • Stochastic Profilers - Utilizes stochastic sampling to synthesize execution data into call graphs for bottleneck identification.
  • Application Performance Profiling - Captures CPU and memory usage from running Go processes over a network to debug production performance.

Historique des stars

Graphique de l'historique des stars pour uber-archive/go-torchGraphique de l'historique des stars pour uber-archive/go-torch

Recherche par IA

Explorez plus de dépôts awesome

Décrivez vos besoins en langage naturel — l'IA classe des milliers de projets open source sélectionnés par pertinence.

Start searching with AI

Alternatives open source à Go Torch

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Go Torch.
  • mstange/samplyAvatar de mstange

    mstange/samply

    4,263Voir sur GitHub↗

    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

    Rust
    Voir sur GitHub↗4,263
  • nswbmw/node-in-debuggingAvatar de nswbmw

    nswbmw/node-in-debugging

    6,457Voir sur GitHub↗

    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

    debugdebuggingguide
    Voir sur GitHub↗6,457
  • google/gopsAvatar de google

    google/gops

    7,011Voir sur GitHub↗

    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

    Go
    Voir sur GitHub↗7,011
  • nvdv/vprofAvatar de nvdv

    nvdv/vprof

    3,979Voir sur GitHub↗

    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

    Pythoncpu-flame-graphd3developer-tools
    Voir sur GitHub↗3,979
Voir les 30 alternatives à Go Torch→

Questions fréquentes

Que fait uber-archive/go-torch ?

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.

Quelles sont les fonctionnalités principales de uber-archive/go-torch ?

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.

Quelles sont les alternatives open-source à uber-archive/go-torch ?

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…