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研究生:鄭隆興
研究生(外文):Lung-Hsing Cheng
論文名稱:一個在異質計算環境下以效能為準的動態迴圈切割法
論文名稱(外文):A performance-based Dynamic Loop Partitioning on heterogeneous computing environments
指導教授:楊朝棟楊朝棟引用關係
指導教授(外文):Chao-Tung Yang
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊工程與科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:48
中文關鍵詞:叢集計算網格計算動態排程迴圈切割平行計算
外文關鍵詞:Parallel loopSelf-schedulingPerformanceMPIPC clusterHeterogeneous
相關次數:
  • 被引用被引用:0
  • 點閱點閱:414
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在平行與分散式系統下,迴圈排程(loop scheduling)成為一個關鍵的論點且變得更困難與異質的環境之間取得平衡點。在過去的文獻中,已經有部分自我排程(self-scheduling)的方案被提出來且適用於異質的環境下。在本篇文章中,我們提出了一個以效能為基準的方法根據每個節點的效能權重來切割迴圈疊代(loop iteration),且建立了一個異質的環境來驗證所提出的方法。在這個實驗中,我們採用了三種不同的應用程式來做比較。實驗結果顯示我們所提出的方法比先前學者所提出的更加有效。
Loop scheduling on parallel and distributed systems has been a critical problem and it is becoming more difficult to deal with in the emerging heterogeneous PC cluster environments. In the past, some self-scheduling loop schemes have been proposed as applicable to heterogeneous cluster environments. In this thesis, we propose a performance-based approach that partitions loop iterations according to the performance weighting of cluster nodes. A heterogeneous cluster was built to verify the proposed approach, and three kinds of application program were implemented for execution on this testbed. Experimental results show that the proposed approach performs better than previous schemes.
摘要 ii
Abstract iii
Acknowledgements iv
Table of Contents v
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Background and related work 4
2.1 Review of loop self-scheduling schemes 4
2.2 Related work 7
2.3 HPC Challenge Benchmark 9
Chapter 3 Proposed Approach 10
3.1 Performance Function 10
3.2 Algorithm 11
3.3 System Model 13
Chapter 4 Experiments and Results 16
4.1 Experiments on Three Applications 16
4.2 Hardware Configuration and Terminology 18
4.3 Experimental Results 20
4.3.1 Application 1: Matrix Multiplication: 20
4.3.2 Application 2: Mandelbrot set computation: 26
4.3.3 Application 3: Circuit Satisfiability: 31
Chapter 5 Conclusion 37
Chapter 6 Bibliography 38
[1] Introduction To The Mandelbrot Set, http://www.ddewey.net/mandelbrot/
[2] The Scalable Computing Laboratory (SCL), http://www.scl.ameslab.gov/
[3] HPC Challenge Benchmark, http://icl.cs.utk.edu/hpcc/
[4] M. Baker and R. Buyya, “Cluster Computing: The Commodity Supercomputer,” International Journal of Software Practice and Experience, Vol. 29, No. 6, 2002, pp.551-575, 1999.
[5] O. Beaumont, H. Casanova, A. Legrand, Y. Robert and Y. Yang, “Scheduling divisible loads on star and tree networks: results and open problems,” IEEE Transactions on Parallel and Distributed Systems, Vol. 16, pp. 207-218, 2005.
[6] B. H. Bennett, E. Davis, T. Kunau and W. Wren, “Beowulf Parallel Processing for Dynamic Load-balancing,” Proceedings on IEEE Aerospace Conference, Vol. 4, 2000, pp. 389-395.
[7] C. A. Bohn and G. B. Lamont, “Load balancing for heterogeneous clusters of PCs,” Future Generation Computer Systems, Vol. 18, pp.389-400, 2002.
[8] Kuan-Wei Cheng, Chao-Tung Yang, Chuan-Lin Lai, and Shun-Chyi Chang, “A Parallel Loop Self-Scheduling on Grid Computing Environments,” Proceedings of the 2004 IEEE International Symposium on Parallel Architectures, Algorithms and Networks, pp. 409-414, KH, China, May 2004.
[9] A. T. Chronopoulos, R. Andonie, M. Benche and D.Grosu, “A Class of Loop Self-Scheduling for Heterogeneous Clusters,” Proceedings of the 2001 IEEE International Conference on Cluster Computing, pp. 282-291, 2001.
[10] S. F. Hummel, E. Schonberg, and L. E. Flynn, “Factoring: a method scheme for scheduling parallel loops,” Communications of the ACM, Vol. 35, pp. 90-101, 1992.
[11] H. Li, S. Tandri, M. Stumm and K. C. Sevcik, “Locality and Loop Scheduling on NUMA Multiprocessors,” Proceedings of the 1993 International Conference on Parallel Processing, vol. II, pp. 140-147, 1993.
[12] C. D. Polychronopoulos and D. Kuck, “Guided Self-Scheduling: a Practical Scheduling Scheme for Parallel Supercomputers,” IEEE Trans. on Computers,
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vol. 36, no. 12, pp. 1425-1439, 1987.
[13] E. Post and H. A. Goosen, “Evaluation the parallel performance of a heterogeneous system,” Proceedings of 5th International Conference and Exhibition on High-Performance Computing in the Asia-Pacific Region (HPC Asia 2001)
[14] Thomas Sterling, Gordon Bell, and Janusz S. Kowalik, “Beowulf Cluster Computing with Linux,” MIT Press, Paperback, Published March 2002.
[15] Wen-Chung Shih, Chao-Tung Yang, and Shian-Shyong Tseng, “A Performance-based Parallel Loop Scheduling on Grid Environments,” The Journal of Supercomputing, vol. 41, no. 3, pp. 247-267, Springer, 2007.
[16] Wen-Chung Shih, Chao-Tung Yang, and Shian-Shyong Tseng, “A Performance-Based Parallel Loop Self-Scheduling on Grid Environments,” Network and Parallel Computing: IFIP International Conference, NPC 2005, Lecture Notes in Computer Science, vol. 3779, pp. 48-55, Springer-Verlag, December 2005.
[17] Wen-Chung Shih, Chao-Tung Yang, and Shian-Shyong Tseng, “A Hybrid Parallel Loop Scheduling Scheme on Grid Environments,” Grid and Cooperative Computing: 4th International Conference, GCC 2005, Lecture Notes in Computer Science, vol. 3795, pp. 370-381, Springer-Verlag, December 2005.
[18] Wen-Chung Shih, Chao-Tung Yang, and Shian-Shyong Tseng, “A Performance-Based Approach to Dynamic Workload Distribution for Master-Slave Applications on Grid Environments,” GPC 2006, Lecture Notes in Computer Science, vol. 3947, pp. 73-82, Springer-Verlag, 2006.
[19] Wen-Chung Shih, Chao-Tung Yang, and Shian-Shyong Tseng, “A Hybrid Parallel Loop Scheduling Scheme on Heterogeneous PC Clusters,” Proceedings of the 6th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2005), pp. 56-58December 5-8, 2005.
[20] P. Tang and P. C. Yew, “Processor self-scheduling for multiple-nested parallel loops,” Proceedings of the 1986 International Conference on Parallel Processing, pp. 528-535, 1986.
[21] T. H. Tzen and L. M. Ni, “Trapezoid self-scheduling: a practical scheduling scheme for parallel compilers,” IEEE Transactions on Parallel and Distributed Systems, Vol. 4, pp. 87-98, 1993.
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[22] Chao-Tung Yang and Shun-Chyi Chang, “A Parallel Loop Self-Scheduling on Extremely Heterogeneous PC Clusters,” Journal of Information Science and Engineering, vol. 20, no. 2, pp. 263-273, March 2004.
[23] Chao-Tung Yang, Kuan-Wei Cheng, and Wen-Chung Shih, “On Development of an Efficient Parallel Loop Self-Scheduling for Grid Computing Environments,” Parallel Computing, vol. 33, no. 7-8, pp. 467-487, 2007.
[24] Chao-Tung Yang, Kuan-Wei Cheng, and Kuan-Ching Li, “An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments,” The Journal of Supercomputing, vol. 34, no. 3, pp. 315-335, Springer, December 2005.
[25] Chao-Tung Yang, Kuan-Wei Cheng, and Kuan-Ching Li, “An Efficient Parallel Loop Self-Scheduling on Grid Environments,” NPC’2004 IFIP International Conference on Network and Parallel Computing, Lecture Notes in Computer Science, Springer-Verlag Heidelberg, Hai Jin, Guangrong Gao, Zhiwei Xu (Eds.), vol. 3222, pp. 92-100, Oct. 2004.
[26] Chao-Tung Yang, Wen-Chung Shih, and Shian-Shyong Tseng, “Dynamic Partitioning of Loop Iterations on Heterogeneous PC Clusters,” The Journal of Supercomputing, vol. 44, no. 1, pp. 1-23, 2007.
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