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wolfpld avatar

wolfpld/tracy

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Tracy

Tracy is a real-time performance profiling framework for C and C++ applications. It provides a software instrumentation library that captures high-resolution telemetry data, which is then visualized through a separate graphical interface to identify bottlenecks and resource allocation issues.

The system utilizes a client-server architecture that enables remote profiling, allowing performance data to be captured on a target machine and analyzed on a workstation. It employs lock-free event logging and shared-memory ring buffers to minimize the overhead of data collection, ensuring that the main application logic remains unaffected during execution.

The toolset covers a broad range of observability capabilities, including the tracking of CPU, GPU, and memory activity, as well as the monitoring of synchronization locks and context switches. It supports the correlation of visual frames with performance events and provides symbol-based callstack resolution to map instruction pointers to source code locations.

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Features

  • Real-Time Performance Profilers - Provides a high-precision, real-time performance analysis tool for C and C++ applications.
  • Performance Profilers - Analyzing execution speed and resource usage in C and C++ applications to identify bottlenecks and optimize code performance.
  • Application Performance Profiling - Captures and visualizes high-resolution telemetry for processor and memory operations to identify bottlenecks.
  • Cross-Platform Monitors - Offers a graphical interface for capturing and analyzing telemetry across multiple threads, processes, and remote machines.
  • Framework Instrumentation - Provides headers and tools for embedding performance tracking directly into source code.
  • Server Execution Profilers - Separates lightweight instrumentation on the target from heavy visualization and analysis on a workstation.
  • Real-Time Monitoring Dashboards - Tracks CPU, GPU, and memory activity in real-time across multiple threads.
  • System Resource Monitors - Tracks memory allocations, synchronization locks, and context switches for comprehensive resource monitoring.
  • Debugging And Tracing - Profiles real-time telemetry for applications.
  • Graphics Development - High-performance frame profiler for game engine development.
  • Lock-Free Thread Communication - Uses atomic operations and thread-local storage to record events without lock contention.
  • Application Debugging Tools - Visualizes task scheduling and lock contention to resolve concurrency issues in multithreaded applications.
  • Remote Profiling Interfaces - Enables capturing performance data on a target machine for analysis on a separate workstation.
  • Symbol Resolution Engines - Maps instruction pointers to source code locations using debug symbols for readable callstacks.
  • Asynchronous Telemetry - Offloads performance data collection to background threads to ensure minimal impact on application execution.
  • Shared Memory Buffers - Employs circular buffers in shared memory to store telemetry events with minimal overhead.
  • Performance Visualization - Allows attaching screenshots to performance frames for visual verification of application state.
15,298 stars·1,012 forks·C++·other·28 views

Star history

Star history chart for wolfpld/tracyStar history chart for wolfpld/tracy

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does wolfpld/tracy do?

Tracy is a real-time performance profiling framework for C and C++ applications. It provides a software instrumentation library that captures high-resolution telemetry data, which is then visualized through a separate graphical interface to identify bottlenecks and resource allocation issues.

What are the main features of wolfpld/tracy?

The main features of wolfpld/tracy are: Real-Time Performance Profilers, Performance Profilers, Application Performance Profiling, Cross-Platform Monitors, Framework Instrumentation, Server Execution Profilers, Real-Time Monitoring Dashboards, System Resource Monitors.

Which projects share features with wolfpld/tracy?

Projects with overlapping indexed features include: kdab/hotspot — Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool.… yse/easy_profiler — Easy Profiler is a performance profiling library for C++ applications designed to measure execution duration and… bloomberg/memray — Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++… brendangregg/flamegraph — FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software… grafana/pyroscope — Pyroscope is a continuous profiling platform designed to collect, store, and visualize application performance data.… jlfwong/speedscope — Speedscope is a web-based performance profiler that visualizes profiling data through interactive flamegraphs and…

Projects sharing features with Tracy

These projects share indexed features with Tracy. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • kdab/hotspotKDAB avatar

    KDAB/hotspot

    5,074View on GitHub↗

    Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool. It functions as a performance profiling visualizer and assembly-level profiler that maps performance costs to specific instructions synchronized with original source code. The project distinguishes itself through a remote symbol resolver that maps performance data from embedded targets to local host debug symbols and sysroots. It also includes a specialized off-CPU analysis tool designed to identify thread wait times and I/O blocks using kernel scheduler tracepoints. The tool

    C++
    View on GitHub↗5,074
  • yse/easy_profileryse avatar

    yse/easy_profiler

    2,360View on GitHub↗

    Easy Profiler is a performance profiling library for C++ applications designed to measure execution duration and identify bottlenecks. It provides a framework for instrumenting code blocks to track performance metrics, allowing for the analysis of thread activity and system behavior through detailed timeline visualization. The library distinguishes itself by utilizing scope-based instrumentation to automatically track code lifecycles, minimizing manual overhead. It employs thread-local buffering and asynchronous data flushing to reduce synchronization contention, ensuring that performance dat

    C++performanceprofilertoolkit
    View on GitHub↗2,360
  • bloomberg/memraybloomberg avatar

    bloomberg/memray

    14,885View on GitHub↗

    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

    Pythonhacktoberfestmemorymemory-leak
    View on GitHub↗14,885
  • brendangregg/flamegraphbrendangregg avatar

    brendangregg/FlameGraph

    19,307View on GitHub↗

    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

    Perl
    View on GitHub↗19,307
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