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研究生:吳松霖
研究生(外文):Song-Lin Wu
論文名稱:利用 Halide 與 MLIR 加速 OpenVX
論文名稱(外文):Accelerating OpenVX through Halide and MLIR
指導教授:廖世偉
指導教授(外文):Shih-Wei Liao
口試委員:張傑帆陳鵬升傅楸善關啟邦
口試委員(外文):Chie-Fan ChangPeng-Sheng ChenChiu-Shan FuChi-Bang Kuan
口試日期:2021-09-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊網路與多媒體研究所
學門:電算機學門
學類:網路學類
論文種類:學術論文
論文出版年:2021
畢業學年度:110
語文別:英文
論文頁數:35
中文關鍵詞:HalideMLIROpenVX影像處理
外文關鍵詞:HalideMLIROpenVXImageProcessing
DOI:10.6342/NTU202103297
相關次數:
  • 被引用被引用:0
  • 點閱點閱:12
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
Verification Letter from the Oral Examination Commitee i
Acknowledgements. ii
摘要 iii
Abstract iv
Contents vi
List of Figures viii
List of Tables. x
Chapter 1 Introduction 1
Chapter 2 Background 3
2.1 OpenVX 3
2.2 Halide 4
2.3 MLIR 6
2.4 Affine Transformation 7
Chapter 3 Motivation 10
Chapter 4 Design and Implementation 11
4.1 OpenVX Transformation 12
4.1.1 OpenVX to MLIR 12
4.1.1.1 Memory Reference(MemRef) 12
4.1.1.2 Affine Representation 13
4.1.1.3 Affine loop tiling 16
4.1.1.4 Vectorization 16
4.1.2 OpenVX to Halide. 18
4.2 Halide Conversion 19
4.2.1 Halide Runtime Library Translator 23
Chapter 5 Evaluation and Discussion 26
5.1 Environment Setup 26
5.2 Experiment Steps 27
5.3 Result 27
5.4 Discussion 30
Chapter 6 Future Work. 31
Chapter 7 Conclusion 32
References 33
[1] Mkl: Math kernel library : https://github.com/oneapi­src/onemkl.
[2] Mlir­hlo: A standalone ”hlo” mlir­based compiler : https://github.com/tensorflow/ mlir­hlo.
[3] Mlirx: https://github.com/polymage­labs/mlirx.
[4] Openvx 1.3 spec: https:// www.khronos.org/ registry/ openvx/ specs/ 1.3/ openvx_specification_1_3.pdf.
[5] Openvx: https://www.khronos.org/openvx.
[6] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghe­ mawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. Tensorflow: A system for large­scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pages 265–283, 2016.
[7] R.Baghdadi,J.Ray,M.B.Romdhane,E.D.Sozzo,A.Akkas,Y.Zhang,P.Suriana, S. Kamil, and S. Amarasinghe. Tiramisu: A polyhedral compiler for expressing fast and portable code, 2018.
[8] C. Bastoul. Code generation in the polyhedral model is easier than you think. In PACT’13 IEEE International Conference on Parallel Architecture and Compilation Techniques, pages 7–16, Juan­les­Pins, France, September 2004.
[9] M.­W. Benabderrahmane, L.­N. Pouchet, A. Cohen, and C. Bastoul. The polyhe­ dral model is more widely applicable than you think. In R. Gupta, editor, Compiler Construction, pages 283–303, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg.
[10] U. Bondhugula. High performance code generation in MLIR: an early case study with GEMM. CoRR, abs/2003.00532, 2020.
[11] K. Goto and R. A. v. d. Geijn. Anatomy of high­performance matrix multiplication. ACM Trans. Math. Softw., 34(3), May 2008.
[12] A. Hartono, M. M. Baskaran, C. Bastoul, A. Cohen, S. Krishnamoorthy, B. Nor­ ris, J. Ramanujam, and P. Sadayappan. Parametric multi­level tiling of imper­ fectly nested loops. In Proceedings of the 23rd International Conference on Supercomputing, ICS ’09, page 147–157, New York, NY, USA, 2009. Association for Computing Machinery.
[13] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. Commun. ACM, 60(6):84–90, May 2017.
[14] C. Lattner, M. Amini, U. Bondhugula, A. Cohen, A. Davis, J. Pienaar, R. Riddle, T. Shpeisman, N. Vasilache, and O. Zinenko. Mlir: Scaling compiler infrastructure for domain specific computation. In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 2–14, 2021.
[15] S. Maleki, Y. Gao, M. J. Garzar ́n, T. Wong, and D. A. Padua. An evaluation of vectorizing compilers. In 2011 International Conference on Parallel Architectures and Compilation Techniques, pages 372–382, 2011.
[16] J. Ragan­Kelley, C. Barnes, A. Adams, S. Paris, F. Durand, and S. Amarasinghe. Halide: A language and compiler for optimizing parallelism, locality, and recompu­ tation in image processing pipelines. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’13, page 519–530, New York, NY, USA, 2013. Association for Computing Machinery.
[17] S. Tavarageri, A. Hartono, M. Baskaran, L.­N. Pouchet, J. Ramanujam, and P. Sa­ dayappan. Parametric tiling of affine loop nests. In Proc. 15th Workshop on Compilers for Parallel Computers. Vienna, Austria, 2010.
[18] S. Verdoolaege. Isl: An integer set library for the polyhedral model. In Proceedings of the Third International Congress Conference on Mathematical Software, ICMS’10, page 299–302, Berlin, Heidelberg, 2010. Springer­Verlag.
[19] Q. Wang, X. Zhang, Y. Zhang, and Q. Yi. Augem: Automatically generate high performance dense linear algebra kernels on x86 cpus. In SC ’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pages 1–12, 2013.
[20] Z. Xianyi, W. Qian, and Z. Yunquan. Model­driven level 3 blas performance opti­ mization on loongson 3a processor. In 2012 IEEE 18th International Conference on Parallel and Distributed Systems, pages 684–691, 2012.
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