Ichnaea: A Framework for Precise Tracking of Memory Objects
Samad Haque, Sibin Mohan, Aaron Paulos, Partha Pal
摘要
Tracing memory objects (who accessed 1 what object and when) is often important for understanding the runtime behavior of modern software. This type of rich per-access metadata can aid in debugging, tracing, forensics and other tasks. Collecting this information is non-trivial since it will be either be incomplete or requires heavy instrumentation and/or hardware support and likely adds significant runtime overheads (e.g., Intel Pin or Valgrind slow programs down by 10 -100x).
We present Ichnaea 2 , a purpose-built, precise and complete framework based on memory protection keys (MPK) that delivers context-rich object events at very low cost to the application. Ichnaea is dormant until one of the objects of interest (ObjOfInterest) is read or written to -at which point it logs any access attempts and changes to the ObjOfInterest along with rich context information ("who is attempting access?", "what changes, if any, are being applied?") before returning control to the application. In general Ichnaea reduces the tracing overheads by 10 -60x when compared to the widely used framework Intel Pin , while still capturing precise, per-access information needed to diagnose memory vulnerabilities, performance hot-spots and subtle concurrency errors.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper1
相关 Paper
- BulletTime: Time Dilation for High-Fidelity TracingMichael Wu, Sibren Isaacman, Abhishek Bhattacharjee, Anurag KhandelwalISCA 2026
- Fine-Grained Kernel Auditing Using Augmented Syscall Reference Behavior Analysis and Virtualized Selective TracingChuqi Zhang, Spencer Faith, Feras Al-Qassas, Theodorus Februanto 等S&P 2026
- Pyriscope: Precise and Low-Overhead Python Control Flow Tracing via Sparse Hardware-Based EventsXinchen Yao, Wu Daiyou, Zhiqiang ZuoOOPSLA 2026
- PROMPT: A Fast and Extensible Memory Profiling FrameworkZiyang Xu, Yebin Chon, Yian Su, Zujun Tan 等OOPSLA 2024 · 被引用 4 次
- LDB: An Efficient Latency Profiling Tool for Multithreaded ApplicationsInho Cho, Seo Jin Park, Ahmed Saeed, Mohammad Alizadeh 等NSDI 2024 · 被引用 4 次
