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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪士灝(Shih-Hao Hung) | |
| dc.contributor.author | Jen-Chieh Wu | en |
| dc.contributor.author | 吳仁捷 | zh_TW |
| dc.date.accessioned | 2021-05-13T08:36:04Z | - |
| dc.date.available | 2016-08-30 | |
| dc.date.available | 2021-05-13T08:36:04Z | - |
| dc.date.copyright | 2016-08-30 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-17 | |
| dc.identifier.citation | [1] Intel vtune performance analyzer. https://software.intel.com/en-us/intel-vtune-amplifier-xe.
[2] I. Baldini, S. J. Fink, and E. Altman. Predicting gpu performance from cpu runs using machine learning. In Computer Architecture and High Performance Computing (SBAC-PAD), 2014 IEEE 26th International Symposium on, pages 254–261. IEEE,2014. [3] F. Bellard. Qemu, a fast and portable dynamic translator. In USENIX Annual Technical Conference, FREENIX Track, pages 41–46, 2005. [4] S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S.-H. Lee, and K. Skadron. Rodinia: A benchmark suite for heterogeneous computing. In Workload Characterization, 2009. IISWC 2009. IEEE International Symposium on, pages 44–54. IEEE,2009. [5] S. L. Graham, P. B. Kessler, and M. K. Mckusick. Gprof: A call graph execution profiler. In ACM Sigplan Notices, volume 17, pages 120–126. ACM, 1982. [6] J. Lau, S. Schoemackers, and B. Calder. Structures for phase classification. In Performance Analysis of Systems and Software, 2004 IEEE International Symposium on-ISPASS, pages 57–67. IEEE, 2004. [7] C.-K. Luk, R. Cohn, R. Muth, H. Patil, A. Klauser, G. Lowney, S. Wallace, V. J.Reddi, and K. Hazelwood. Pin: building customized program analysis tools with dynamic instrumentation. In ACM Sigplan Notices, volume 40, pages 190–200. ACM, 2005. [8] Y. Luo, V. Packirisamy, W.-C. Hsu, A. Zhai, N. Mungre, and A. Tarkas. Dynamic performance tuning for speculative threads. In ACM SIGARCH Computer Architecture News, volume 37, pages 462–473. ACM, 2009. [9] C. Olschanowsky, A. Snavely, M. R. Meswani, and L. Carrington. Pir: Pmac’s idiom recognizer. In 2010 39th International Conference on Parallel Processing Workshops, pages 189–196. IEEE, 2010. [10] E. Perelman, G. Hamerly, and B. Calder. Picking statistically valid and early simulation points. In Parallel Architectures and Compilation Techniques, 2003. PACT 2003. Proceedings. 12th International Conference on, pages 244–255. IEEE, 2003. [11] B. Reagen, R. Adolf, Y. S. Shao, G.-Y. Wei, and D. Brooks. Machsuite: Benchmarks for accelerator design and customized architectures. In Workload Characterization (IISWC), 2014 IEEE International Symposium on, pages 110–119. IEEE, 2014. [12] Y. Sato, Y. Inoguchi, and T. Nakamura. On-the-fly detection of precise loop nests across procedures on a dynamic binary translation system. In Proceedings of the 8th ACM International Conference on Computing Frontiers, page 25. ACM, 2011. [13] A. Sembrant, D. Eklov, and E. Hagersten. Efficient software-based online phase classification. In Workload Characterization (IISWC), 2011 IEEE International Symposium on, pages 104–115. IEEE, 2011. [14] T. Sherwood, E. Perelman, G. Hamerly, and B. Calder. Automatically characterizing large scale program behavior. In ACM SIGARCH Computer Architecture News, volume 30, pages 45–57. ACM, 2002. [15] T. Sherwood, S. Sair, and B. Calder. Phase tracking and prediction. In ACM SIGARCH Computer Architecture News, volume 31, pages 336–349. ACM, 2003. [16] T. Sondag and H. Rajan. Phase-guided thread-to-core assignment for improved utilization of performance-asymmetric multi-core processors. In Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering, pages 73–80. IEEE Computer Society, 2009. [17] C.-H. Tu, H.-H. Hsu, J.-H. Chen, C.-H. Chen, and S.-H. Hung. Performance and power profiling for emulated android systems. ACM Transactions on Design Automation of Electronic Systems (TODAES), 19(2):10, 2014. [18] L. Wang, S. L. Jacques, and L. Zheng. Mcml—monte carlo modeling of light transport in multi-layered tissues. Computer methods and programs in biomedicine, 47(2):131–146, 1995. [19] Wikipedia. Backpropagation — wikipedia, the free encyclopedia, 2016. [Online; accessed 2-August-2016]. [20] Wikipedia. Knuth–morris–pratt algorithm — wikipedia, the free encyclopedia, 2016. [Online; accessed 3-August-2016]. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3704 | - |
| dc.description.abstract | 面對不熟悉的程式時,程式行為分析工具扮演著重要的角色。面對複雜的程式,能夠針對程式各段不同的行為做出分析,也成為重要的課題。先前的研究聚焦在分析程式的各個函式或是迴圈,然而這樣的分析忽略了在函式及迴圈的內部也存在著不同的行為。
在這份研究中,我們使用程式相態判別(Program Phase Detection)的技術做為將程式切分的依據,並建立了以此為基礎的分析工具。更進一步,我們藉由機器學習預測每個程式相態的GPU友善度,驗證了程式相態在某些高階特徵上確實存在群聚效果,其預測準確度為94%。最後我們藉由實際的例子,說明以程式相態為基礎的分析工具確實可以偵測出函式及迴圈內部的不同行為。 | zh_TW |
| dc.description.abstract | When we first met an unfamiliar program, the profiling tool plays an important role in understanding program behavior. As software applications become more and more complicated, to profile each section with different behavior in the program is crucial. Previous studies focus on profiling each function or loop in the program, however, which might ignore the fact that different behaviors happen inside a loop or a function.
In this study, we use program phase detection to partition the program and build up a profiling tool based on it. Furthermore, we verified the grouping effect of program phases on some high-level features by predicting GPU friendliness of each program phase with machine learning model. The accuracy of prediction comes to 94%. At last, we take real applications as test cases, showing that the profiling tool based on program phase detection is able to detect behavior changing inside a loop or a function. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-13T08:36:04Z (GMT). No. of bitstreams: 1 ntu-105-R03922116-1.pdf: 2982960 bytes, checksum: aa6b0b812ec99ad105b475c6a5f0fe4f (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Background and Related Work 4 2.1 Program Phase Detection . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 QEMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.1 Virtual Performance Monitoring Units (VPMU) . . . . . . . . 5 2.3 Predicting GPU Performance Using Machine Learning . . . . . . . . 6 2.4 PIR: PMaC’s Idiom Recognizer . . . . . . . . . . . . . . . . . . . . 7 2.5 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 3 Methodology 10 3.1 Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Program Phase Detection in VPMU . . . . . . . . . . . . . . . . . . . 11 3.3 Grouping Effect on High Level Features . . . . . . . . . . . . . . . . . 12 3.3.1 GPU Friendliness Prediction . . . . . . . . . . . . . . . . . . . 13 Chapter 4 Evaluation 15 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 GPU Friendliness Prediction . . . . . . . . . . . . . . . . . . . . . . . 15 4.2.1 Results of Prediction Model . . . . . . . . . . . . . . . . . . . 16 4.3 Comparing with Function-Based Profiling . . . . . . . . . . . . . . . 17 4.4 Case Study: Backpropagation . . . . . . . . . . . . . . . . . . . . . . 18 4.5 Case Study: MCML . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.6 Case Study: KMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 5 Conclusion and Future Work 28 5.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Bibliography 30 | |
| dc.language.iso | en | |
| dc.title | 基於程式相態特性的異質系統效能分析 | zh_TW |
| dc.title | Characterization of Program Phases for Heterogeneous Systems with Virtual Platforms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 涂嘉恆(Chia-Heng Tu),廖世偉(Shih-Wei Liao) | |
| dc.subject.keyword | 程式相態,程式分析,裝置友善度, | zh_TW |
| dc.subject.keyword | Program phases,Profiling tool,GPU friendliness, | en |
| dc.relation.page | 32 | |
| dc.identifier.doi | 10.6342/NTU201602721 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2016-08-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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