awesome-repositories.com
المدونة
awesome-repositories.com

اكتشف أفضل مستودعات المصادر المفتوحة باستخدام بحث مدعوم بالذكاء الاصطناعي.

استكشفعمليات بحث منسقةبدائل مفتوحة المصدربرمجيات ذاتية الاستضافةالمدونةخريطة الموقع
المشروعحولكيفية ترتيب النتائجالصحافةخادم MCP
قانونيالخصوصيةالشروط
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

1 مستودع

Awesome GitHub RepositoriesProfile-Based Stack Reconstructors

Tools that retrieve and assemble full call stacks from profiling data using frame and callsite identifiers.

Distinct from Asynchronous Stack Trace Reconstruction: Distinct from Asynchronous Stack Trace Reconstruction: reconstructs stacks from profiling data using frame identifiers, not async execution boundaries.

Explore 1 awesome GitHub repository matching development tools & productivity · Profile-Based Stack Reconstructors. Refine with filters or upvote what's useful.

Awesome Profile-Based Stack Reconstructors GitHub Repositories

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • google/perfettoالصورة الرمزية لـ google

    google/perfetto

    5,558عرض على GitHub↗

    Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

    Retrieves and assembles full call stacks from profiling data using frame and callsite identifiers.

    C++
    عرض على GitHub↗5,558
  1. Home
  2. Development Tools & Productivity
  3. Asynchronous Debugging Toolkits
  4. Asynchronous Stack Trace Reconstruction
  5. Profile-Based Stack Reconstructors