Awesome GitHub RepositoriesFlame Graph Generators

FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari

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.

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

Parca is an always-on continuous profiling platform that captures CPU and memory usage from running applications without any code modifications. It uses eBPF kernel-level tracing to automatically discover and sample stack traces across infrastructure, and provides a web-based flame graph dashboard for interactive performance analysis. Its label-based query engine lets users slice and aggregate profiling data across dimensions such as service, container, or region, using a Prometheus-style selector syntax. Unlike basic profilers, Parca stores profile samples in a columnar format using Apache A

go-torch is a profiling tool for capturing the execution state of Go programs and transforming raw binary data into visual representations of program performance. It functions as a flame graph profiler and performance visualization utility that identifies expensive code paths through the collection of CPU and memory stack traces. The tool features a network-capable remote process profiler that connects to endpoints to capture and export execution profiles from Go binaries. It utilizes stochastic profiling to synthesize execution data into call graphs, allowing for the identification of bottle