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JPortal: precise and efficient control-flow tracing for JVM programs with Intel processor trace

Authors:

Guoqing Harry XuAuthors Info & Claims

PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

Pages 1080 - 1094

https://doi.org/10.1145/3453483.3454096

Published: 18 June 2021 Publication History

Abstract

Hardware tracing modules such as Intel Processor Trace perform continuous control-flow tracing of an end-to-end program execution with an ultra-low overhead. PT has been used in a variety of contexts to support applications such as testing, debugging, and performance diagnosis. However, these hardware modules have so far been used only to trace native programs, which are directly compiled down to machine code. As high-level languages (HLL) such as Java and Go become increasingly popular, there is a pressing need to extend these benefits to the HLL community. This paper presents JPortal, a JVM-based profiling tool that bridges the gap between HLL applications and low-level hardware traces by using a set of algorithms to precisely recover an HLL program’s control flow from PT traces. An evaluation of JPortal with the DaCapo benchmark shows that JPortal achieves an overall 80% accuracy for end-to-end control flow profiling with only a 4-16% runtime overhead.

References

[1]

[n.d.]. ASM: an open-source Java bytecode manipulation and analysis framework. https://asm.ow2.io/ Accessed: 2020-10-31.

[2]

[n.d.]. DiSL: an open-source Java bytecode instrumentation framework. https://disl.ow2.org/view/Main/ Accessed: 2020-10-31.

[3]

[n.d.]. DynamoRIO: a runtime code manipulation system. https://dynamorio.org/ Accessed: 2020-10-31.

[4]

[n.d.]. Dyninst: tools for binary instrumentation, analysis, and modification. https://dyninst.org/ Accessed: 2020-10-31.

[5]

[n.d.]. Honggfuzz: a feedback-driven security oriented software fuzzer. http://honggfuzz.com Accessed: 2020-10-31.

[6]

[n.d.]. hprof: an open source java profiler. http://java.sun.com/developer/technicalArticles/Programming/HPROF.html Accessed: 2020-10-31.

[7]

[n.d.]. Intel® VTune™ Profiler: a commercial application for software performance analysis. https://software.intel.com/content/www/us/en/develop/tools/vtune-profiler.html Accessed: 2020-10-31.

[8]

[n.d.]. JProfiler: a commercial java profiler. https://www.ej-technologies.com/products/jprofiler/overview.html Accessed: 2020.

[9]

[n.d.]. libipt: an Intel(R) Processor Trace decoder library. https://github.com/intel/libipt Accessed: 2020-10-31.

[10]

[n.d.]. LLVM: a compiler infrastructure. https://llvm.org/ 2021.

[11]

[n.d.]. perf: a performance analyzing tool in Linux. https://github.com/torvalds/linux/tree/master/tools/perf Accessed: 2020-10-31.

[12]

[n.d.]. Pin: a dynamic binary instrumentation framework. http://www.intel.com/software/pintool Accessed: 2020-10-31.

[13]

[n.d.]. ptm2human: a decoder for trace data outputted by Embedded Trace Macrocell (ETMv4). https://github.com/hwangcc23/ptm2human Accessed: 2020-10-31.

[14]

[n.d.]. Soot: an open-source Java compiler infrastructure. https://github.com/soot-oss/soot Accessed: 2020-10-31.

[15]

[n.d.]. Valgrind: an instrumentation framework for building dynamic analysis tools. https://valgrind.org/ Accessed: 2020-10-31.

[16]

[n.d.]. xprof: the internal profiler for hotspot. http://java.sun.com/docs/books/performance/1st edition/html/JPAppHotspot.fm.html Accessed: 2020-10-31.

[17]

[n.d.]. YourKit: a commercial java profiler. https://www.yourkit.com/ Accessed: 2020-10-31.

[18]

2019. Arm® Embedded Trace Macrocell Architecture Specification ETMv4.0 to ETMv4.5.

[19]

2019. Intel® 64 and IA-32 Architectures Software Developer’s Manual Volume 3 (3A, 3B, 3C & 3D): System Programming Guide.

[20]

Glenn Ammons, Thomas Ball, and James R. Larus. 1997. Exploiting Hardware Performance Counters with Flow and Context Sensitive Profiling. In Proceedings of the ACM SIGPLAN 1997 Conference on Programming Language Design and Implementation (PLDI ’97). 85–96. isbn:0897919076 https://doi.org/10.1145/258915.258924

Digital Library

[21]

Matthew Arnold and Barbara G. Ryder. 2001. A Framework for Reducing the Cost of Instrumented Code. In Proceedings of the ACM SIGPLAN 2001 Conference on Programming Language Design and Implementation (PLDI ’01). 168–179. isbn:1581134142 https://doi.org/10.1145/378795.378832

Digital Library

[22]

Joy Arulraj, Po-Chun Chang, Guoliang Jin, and Shan Lu. 2013. Production-Run Software Failure Diagnosis via Hardware Performance Counters. In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’13). 101–112. isbn:9781450318709 https://doi.org/10.1145/2451116.2451128

Digital Library

[23]

Joy Arulraj, Guoliang Jin, and Shan Lu. 2014. Leveraging the Short-Term Memory of Hardware to Diagnose Production-Run Software Failures. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’14). 207–222. isbn:9781450323055 https://doi.org/10.1145/2541940.2541973

Digital Library

[24]

Thomas Ball and James R. Larus. 1994. Optimally Profiling and Tracing Programs. ACM Trans. Program. Lang. Syst., 16, 4 (1994), July, 1319–1360. issn:0164-0925 https://doi.org/10.1145/183432.183527

Digital Library

[25]

Thomas Ball and James R. Larus. 1996. Efficient Path Profiling. In Proceedings of the 29th Annual ACM/IEEE International Symposium on Microarchitecture (MICRO 29). 46–57. isbn:0818676418

[26]

S. M. Blackburn, R. Garner, C. Hoffman, A. M. Khan, K. S. McKinley, R. Bentzur, A. Diwan, D. Feinberg, D. Frampton, S. Z. Guyer, M. Hirzel, A. Hosking, M. Jump, H. Lee, J. E. B. Moss, A. Phansalkar, D. Stefanović, T. VanDrunen, D. von Dincklage, and B. Wiedermann. 2006. The DaCapo Benchmarks: Java Benchmarking Development and Analysis. In OOPSLA ’06: Proceedings of the 21st annual ACM SIGPLAN conference on Object-Oriented Programing, Systems, Languages, and Applications. 169–190. https://doi.org/10.1145/1167473.1167488

Digital Library

[27]

Cheng Cai, Qirun Zhang, Zhiqiang Zuo, Khanh Nguyen, Guoqing Xu, and Zhendong Su. 2018. Calling-to-Reference Context Translation via Constraint-Guided CFL-Reachability. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2018). 196–210. isbn:9781450356985 https://doi.org/10.1145/3192366.3192378

Digital Library

[28]

Yaohui Chen, Dongliang Mu, Jun Xu, Zhichuang Sun, Wenbo Shen, Xinyu Xing, Long Lu, and Bing Mao. 2019. PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary. In Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security (Asia CCS ’19). 633–645. isbn:9781450367523 https://doi.org/10.1145/3321705.3329828

Digital Library

[29]

Edmund Clarke, Orna Grumberg, Somesh Jha, Yuan Lu, and Helmut Veith. 2003. Counterexample-Guided Abstraction Refinement for Symbolic Model Checking. J. ACM, 50, 5 (2003), Sept., 752–794. issn:0004-5411 https://doi.org/10.1145/876638.876643

Digital Library

[30]

Weidong Cui, Xinyang Ge, Baris Kasikci, Ben Niu, Upamanyu Sharma, Ruoyu Wang, and Insu Yun. 2018. REPT: Reverse Debugging of Failures in Deployed Software. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI’18). 17–32. isbn:9781931971478

[31]

Cormac Flanagan and Stephen N. Freund. 2010. The RoadRunner Dynamic Analysis Framework for Concurrent Programs. In Proceedings of the 9th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering (PASTE ’10). 1–8. isbn:9781450300827 https://doi.org/10.1145/1806672.1806674

Digital Library

[32]

Xinyang Ge, Weidong Cui, and Trent Jaeger. 2017. GRIFFIN: Guarding Control Flows Using Intel Processor Trace. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’17). 585–598. isbn:9781450344654 https://doi.org/10.1145/3037697.3037716

Digital Library

[33]

Xinyang Ge, Ben Niu, and Weidong Cui. 2020. Reverse Debugging of Kernel Failures in Deployed Systems. In 2020 USENIX Annual Technical Conference, USENIX ATC 2020, July 15-17, 2020, Ada Gavrilovska and Erez Zadok (Eds.). 281–292. https://www.usenix.org/conference/atc20/presentation/ge

[34]

Baris Kasikci, Weidong Cui, Xinyang Ge, and Ben Niu. 2017. Lazy Diagnosis of In-Production Concurrency Bugs. In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP ’17). 582–598. isbn:9781450350853 https://doi.org/10.1145/3132747.3132767

Digital Library

[35]

James R. Larus. 1999. Whole Program Paths. In Proceedings of the ACM SIGPLAN 1999 Conference on Programming Language Design and Implementation (PLDI ’99). 259–269. isbn:1581130945 https://doi.org/10.1145/301618.301678

Digital Library

[36]

Jeremy Lau, Matthew Arnold, Michael Hind, and Brad Calder. 2006. Online Performance Auditing: Using Hot Optimizations without Getting Burned. In Proceedings of the 27th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’06). 239–251. isbn:1595933204 https://doi.org/10.1145/1133981.1134010

Digital Library

[37]

Han Bok Lee and Benjamin G. Zorn. 1997. BIT: A Tool for Instrumenting Java Bytecodes. In USENIX Symposium on Internet Technologies and Systems (USITS 97). https://www.usenix.org/conference/usits-97/bit-tool-instrumenting-java-bytecodes

[38]

Daniel Lehmann and Michael Pradel. 2019. Wasabi: A Framework for Dynamically Analyzing WebAssembly. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’19). 1045–1058. isbn:9781450362405 https://doi.org/10.1145/3297858.3304068

Digital Library

[39]

Y. Liu, P. Shi, X. Wang, H. Chen, B. Zang, and H. Guan. 2017. Transparent and Efficient CFI Enforcement with Intel Processor Trace. In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA). 529–540. https://doi.org/10.1109/HPCA.2017.18

[40]

Chi-Keung Luk, Robert Cohn, Robert Muth, Harish Patil, Artur Klauser, Geoff Lowney, Steven Wallace, Vijay Janapa Reddi, and Kim Hazelwood. 2005. Pin: Building Customized Program Analysis Tools with Dynamic Instrumentation. In Proceedings of the 2005 ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’05). 190–200. isbn:1595930566 https://doi.org/10.1145/1065010.1065034

Digital Library

[41]

Lukáš Marek, Alex Villazón, Yudi Zheng, Danilo Ansaloni, Walter Binder, and Zhengwei Qi. 2012. DiSL: A Domain-Specific Language for Bytecode Instrumentation. In Proceedings of the 11th Annual International Conference on Aspect-Oriented Software Development (AOSD ’12). 239–250. isbn:9781450310925 https://doi.org/10.1145/2162049.2162077

Digital Library

[42]

Todd Mytkowicz, Amer Diwan, Matthias Hauswirth, and Peter F. Sweeney. 2010. Evaluating the Accuracy of Java Profilers. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’10). 187–197. isbn:9781450300193 https://doi.org/10.1145/1806596.1806618

Digital Library

[43]

Khanh Nguyen and Guoqing Xu. 2013. Cachetor: Detecting Cacheable Data to Remove Bloat. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2013). 268–278. isbn:9781450322379 https://doi.org/10.1145/2491411.2491416

Digital Library

[44]

Z. Ning and F. Zhang. 2019. Hardware-Assisted Transparent Tracing and Debugging on ARM. IEEE Transactions on Information Forensics and Security, 14, 6 (2019), 1595–1609. https://doi.org/10.1109/TIFS.2018.2883027

Digital Library

[45]

Peter Ohmann, Alexander Brooks, Loris D’Antoni, and Ben Liblit. 2017. Control-flow recovery from partial failure reports. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2017, Barcelona, Spain, June 18-23, 2017, Albert Cohen and Martin T. Vechev (Eds.). 390–405. https://doi.org/10.1145/3062341.3062368

Digital Library

[46]

Tao B. Schardl, Tyler Denniston, Damon Doucet, Bradley C. Kuszmaul, I-Ting Angelina Lee, and Charles E. Leiserson. 2017. The CSI Framework for Compiler-Inserted Program Instrumentation. Proc. ACM Meas. Anal. Comput. Syst., 1, 2 (2017), Article 43, Dec., 25 pages. https://doi.org/10.1145/3154502

Digital Library

[47]

Sergej Schumilo, Cornelius Aschermann, Robert Gawlik, Sebastian Schinzel, and Thorsten Holz. 2017. kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels. In 26th USENIX Security Symposium (USENIX Security 17). 167–182. isbn:978-1-931971-40-9 https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/schumilo

Digital Library

[48]

Koushik Sen, Swaroop Kalasapur, Tasneem Brutch, and Simon Gibbs. 2013. Jalangi: A Selective Record-Replay and Dynamic Analysis Framework for JavaScript. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2013). 488–498. isbn:9781450322379 https://doi.org/10.1145/2491411.2491447

Digital Library

[49]

Ajeet Shankar, Matthew Arnold, and Rastislav Bodik. 2008. Jolt: Lightweight Dynamic Analysis and Removal of Object Churn. In Proceedings of the 23rd ACM SIGPLAN Conference on Object-Oriented Programming Systems Languages and Applications (OOPSLA ’08). 127–142. isbn:9781605582153 https://doi.org/10.1145/1449764.1449775

Digital Library

[50]

Suchakra Sharma. 2015. Hardware Tracing for Fast and Precise Performance Analysis. The New Stack, 10.

[51]

S. D. Sharma and M. Dagenais. 2016. Hardware-assisted instruction profiling and latency detection. The Journal of Engineering, 2016, 10 (2016), 367–376. https://doi.org/10.1049/joe.2016.0127

[52]

Chukri Soueidi, Ali Kassem, and Yliès Falcone. 2020. BISM: Bytecode-Level Instrumentation for Software Monitoring. In Runtime Verification, Jyotirmoy Deshmukh and Dejan Ničković (Eds.). 323–335. isbn:978-3-030-60508-7

[53]

Neal Stollon. 2011. ARM ETM. Springer US, Boston, MA. 213–218. isbn:978-1-4419-7563-8 https://doi.org/10.1007/978-1-4419-7563-8_13

[54]

The same crashes encountered as reported by the following issues when running certain subjects on OpenJDK 12. 2020. https://github.com/dacapobench/dacapobench/issues/175;

[55]

Xiayang Wang, Fuqian Huang, and Haibo Chen. 2019. DTrace: fine-grained and efficient data integrity checking with hardware instruction tracing. Cybersecur., 2, 1 (2019), 1. https://doi.org/10.1186/s42400-018-0018-3

[56]

Mario Wolczko and Cansu Kaynak. 2017. Processor Tracing for Virtual Machines. In The Workshop on Modern Language Runtimes, Ecosystems, and VMs (MoreVMs).

[57]

Guoqing Xu. 2012. Finding Reusable Data Structures. In Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA ’12). 1017–1034. isbn:9781450315616 https://doi.org/10.1145/2384616.2384690

Digital Library

[58]

Guoqing Xu, Matthew Arnold, Nick Mitchell, Atanas Rountev, and Gary Sevitsky. 2009. Go with the Flow: Profiling Copies to Find Runtime Bloat. In Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’09). 419–430. isbn:9781605583921 https://doi.org/10.1145/1542476.1542523

Digital Library

[59]

Guoqing Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, Edith Schonberg, and Gary Sevitsky. 2010. Finding Low-Utility Data Structures. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’10). 174–186. isbn:9781450300193 https://doi.org/10.1145/1806596.1806617

Digital Library

[60]

Hao Xu, Qingsen Wang, Shuang Song, Lizy Kurian John, and Xu Liu. 2019. Can We Trust Profiling Results? Understanding and Fixing the Inaccuracy in Modern Profilers. In Proceedings of the ACM International Conference on Supercomputing (ICS ’19). 284–295. isbn:9781450360791 https://doi.org/10.1145/3330345.3330371

Digital Library

[61]

Yibiao Yang, Yanyan Jiang, Zhiqiang Zuo, Yang Wang, Hao Sun, Hongmin Lu, Yuming Zhou, and Baowen Xu. 2019. Automatic Self-Validation for Code Coverage Profilers. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE ’19). 79–90. isbn:9781728125084 https://doi.org/10.1109/ASE.2019.00018

Digital Library

[62]

Yibiao Yang, Yuming Zhou, Hao Sun, Zhendong Su, Zhiqiang Zuo, Lei Xu, and Baowen Xu. 2019. Hunting for Bugs in Code Coverage Tools via Randomized Differential Testing. In Proceedings of the 41st International Conference on Software Engineering (ICSE ’19). 488–499. https://doi.org/10.1109/ICSE.2019.00061

Digital Library

[63]

G. Zhang, X. Zhou, Y. Luo, X. Wu, and E. Min. 2018. PTfuzz: Guided Fuzzing With Processor Trace Feedback. IEEE Access, 6 (2018), 37302–37313. https://doi.org/10.1109/ACCESS.2018.2851237

[64]

Zhiqiang Zuo, Lu Fang, Siau-Cheng Khoo, Guoqing Xu, and Shan Lu. 2016. Low-Overhead and Fully Automated Statistical Debugging with Abstraction Refinement. In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2016). 881–896. isbn:9781450344449 https://doi.org/10.1145/2983990.2984005

Digital Library

[65]

Zhiqiang Zuo, Siau-Cheng Khoo, and Chengnian Sun. 2014. Efficient Predicated Bug Signature Mining via Hierarchical Instrumentation. In Proceedings of the 2014 International Symposium on Software Testing and Analysis (ISSTA 2014). 215–224. isbn:9781450326452 https://doi.org/10.1145/2610384.2610400

Digital Library

Cited By

Zhang YLiu TWang YQi YJi KTang JWang XLi XZuo Z(2024)HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware TracingProceedings of the ACM on Programming Languages10.1145/36897688:OOPSLA2(1615-1640)Online publication date: 8-Oct-2024
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Yue TJin YZhang FNing ZWang PZhou XLu K(2024)Efficiently Rebuilding Coverage in Hardware-Assisted Greybox FuzzingProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678933(450-464)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678933
Yue TZhang FNing ZWang PZhou XLu KZhou L(2024)Armor: Protecting Software Against Hardware Tracing TechniquesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337281619(4247-4262)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3372816
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Index Terms

JPortal: precise and efficient control-flow tracing for JVM programs with Intel processor trace
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. Software maintenance tools
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 2021

1341 pages

ISBN:9781450383912

DOI:10.1145/3453483

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Williams College, USA
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Technion, Israel

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Cited By

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Yue TJin YZhang FNing ZWang PZhou XLu K(2024)Efficiently Rebuilding Coverage in Hardware-Assisted Greybox FuzzingProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678933(450-464)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678933
Yue TZhang FNing ZWang PZhou XLu KZhou L(2024)Armor: Protecting Software Against Hardware Tracing TechniquesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337281619(4247-4262)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3372816
Park YChoi SChoi UJin HNor NPark Y(2024)A practical approach for finding anti-debugging routines in the Arm-Linux using hardware tracingScientific Reports10.1038/s41598-024-65374-w14:1Online publication date: 26-Jun-2024
https://doi.org/10.1038/s41598-024-65374-w
Liu XYang CLi DZhou YLi SChen JChen Z(2023)Adonis: Practical and Efficient Control Flow Recovery through OS-level TracesACM Transactions on Software Engineering and Methodology10.1145/360718733:1(1-27)Online publication date: 24-Nov-2023
https://dl.acm.org/doi/10.1145/3607187

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