Awesome GitHub RepositoriesFlame Graph Visualizers

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

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

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 是一套性能工具，旨在对 Java 运行时进行采样、跟踪内存分配并监控硬件计数器。它充当 Java 应用程序的低开销采样分析器，在没有安全点偏差的情况下收集堆栈跟踪和内存分配数据。 该项目提供用于生成交互式火焰图的专用实用程序，以在 Web 浏览器中可视化执行热点。它包括一个硬件性能计数器监视器，用于跟踪低级系统事件，如缓存未命中和页面错误。 该工具集涵盖了多个诊断领域，包括用于识别热点方法的 CPU 使用率分析、用于堆和原生内存泄漏的内存分配跟踪，以及用于揭示不同系统层之间同步瓶颈的线程争用分析。

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

gops 是一个命令行诊断工具集，用于监控、分析和管理活跃 Go 应用的运行时状态。它作为一个运行时诊断工具，提供了一个专注的界面，用于分析内存、分析性能和监控运行进程的健康状况。 该工具提供了一组专门的实用程序，包括用于捕获 CPU 和堆分析文件的性能分析器、用于识别泄漏并触发垃圾回收的内存分析器，以及用于发现运行中的二进制文件并可视化进程层次结构的进程监控器。 该项目涵盖了广泛的诊断功能，包括运行时执行追踪、堆栈跟踪捕获和资源使用采样。它还包括用于进程元数据检查、二进制路径解析以及监控线程数和内存统计等运行时指标的系统工具。

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

本项目是 Go 编程语言综合指南的中文翻译版。它作为一个本地化的教育资源和技术手册，旨在为语言语法、设计和软件开发提供指导。 该资源涵盖了广泛的 Go 语言教育内容，包括编程模式的实现和系统设计。它包含翻译后的课程和示例，重点关注并发和接口使用等核心语言特性。 内容跨越了多个能力领域，包括语言基础、数据建模、运行时反射和内存管理。它还详细介绍了软件架构、错误处理、质量保证和 Web 网络编程。 该文档结构为技术手册，包含翻译内容、勘误和修正，以确保学习的准确性。

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

PerfView is a set of integrated profiling and tracing tools built on Windows Event Tracing for Windows (ETW), designed to diagnose CPU, memory, and ETW-based performance issues in .NET applications. It captures ETW events to analyze runtime behavior, CPU usage, and memory allocations, serving as a .NET performance profiler that measures both CPU time and garbage collection events. The tool distinguishes itself with a diff comparison engine that compares two performance traces side-by-side to highlight changes in method costs and event rates. It renders profiled call stacks as flame graphs whe

Samply 是一个跨平台 CPU 采样分析器和性能分析实用程序。它由一个用于定期记录进程堆栈跟踪的命令行工具和一个用于分析所得执行数据的可视化界面组成。 该系统包括一个调试符号解析器，使用本地或远程符号信息将原始内存地址映射为人类可读的函数名称。它将记录的执行数据转换为火焰图和时间线，以精确定位函数级热点。 该工具提供跨 macOS、Linux 和 Windows 的 CPU 执行记录、堆栈展开和符号解析功能。它利用操作系统 API 附加到正在运行的进程并识别处理瓶颈。

go-torch 是一个性能分析工具，用于捕获 Go 程序的执行状态并将原始二进制数据转换为程序性能的可视化表示。它作为火焰图分析器和性能可视化工具，通过收集 CPU 和内存堆栈跟踪来识别昂贵的代码路径。 该工具具有支持网络的远程进程分析器，可连接到端点以从 Go 二进制文件中捕获和导出执行配置文件。它利用随机分析将执行数据合成到调用图中，从而识别运行中应用程序的瓶颈。 该项目涵盖了从堆栈跟踪采样和远程配置文件获取到调用图合成的完整分析数据收集流水线。这些工作流最终生成基于 SVG 的火焰图，可视化函数深度和执行频率。

该项目是 Go 语言的综合性能编程指南和参考，专注于运行时效率和内存优化。它提供了一系列模式和技术，旨在通过减少垃圾回收开销和优化内存使用来提高执行速度。 该资源通过详细的内存优化参考实现脱颖而出，例如逃逸分析、对象池和结构体内存对齐。它提供了减少二进制大小和通过结构体内存布局优化及使用零分配占位符来提高 CPU 缓存效率的具体策略。 该项目涵盖了广泛的后端工程功能，包括使用工作池和同步原语的并发管理、高性能 RPC 和 HTTP 路由以及分布式缓存策略。它还包括通过 CPU 和内存分析进行可观测性的指南，以及用于功能单元测试和模拟对象生成的质量保证模式。 内容结构化为一系列教程、架构示例和基准测试指南，以帮助开发者分析和修复性能瓶颈。