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Improvement of workload balancing using parallel loop self-scheduling on Intel Xeon Phi

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Abstract

In recent years, Intel promotes its new product Xeon Phi coprocessor, which is similar to the x86 architecture coprocessor. It has about 60 cores and can be regarded as a single computing node, with the computing power that cannot be ignored. This work aims to improve the workload balance by parallel loop self-scheduling scheme performed on Xeon Phi-based computer cluster. The proposed concept is implemented by hybrid MPI and OpenMP parallel programming in C language. Since parallel loop self-scheduling composes of static and dynamic allocation, weighting algorithm is adopted in the static part, while the well-known loop self-scheduling is adopted in dynamic part. The loop block is partitioned according to the weighting of MIC and HOST nodes. Accordingly, Xeon Phi with many-core is adopted to implement parallel loop self-scheduling. Finally, we test the performance in the experiments by four applicable problems: matrix multiplication, sparse matrix multiplication, Mandelbrot set and circuit meet. The experimental results indicate how to do the weight allocation and which scheduling method can achieve the best performance.

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References

  1. Heinecke A (2013) Accelerators in scientific computing is it worth the effort? In: 2013 International Conference on High Performance Computing and Simulation (HPCS), p 504

  2. Rosales C (2013) Porting to the intel xeon phi: opportunities and challenges. In: Extreme Scaling Workshop, pp 1–7

  3. Hwu W mei (2014) What is ahead for parallel computing. J Parallel Distrib Comput 74:2574–2581

    Article  Google Scholar 

  4. Andrew M, Justin R, Alan G, Herman L (2014) A multi-tiered optimization framework for heterogeneous computing. In: High Performance Extreme Computing Conference (HPEC), IEEE, pp 1–6

  5. Yang C-T, Shih W-C, Tseng S-S (2007) Dynamic partitioning of loop iterations on heterogeneous pc clusters. J Supercomput 44:1–23

    Article  Google Scholar 

  6. Yang C-T, Shih W-C, Cheng L-H (2012) Performance-based dynamic loop scheduling in heterogeneous computing environments. J Supercomput 59:414. doi:10.1007/s11227-010-0443-x

  7. Wu C-C, Yang C-T, Lai K-C, Chiu P-H (2012) Designing parallel loop self-scheduling schemes using the hybrid mpi and openmp programming model for multi-core grid systems. J Supercomput 59:42–60

    Article  Google Scholar 

  8. Shih W-C, Yang C-T, Tseng S-S (2007) A performance-based parallel loop scheduling on grid environments. J Supercomput 41:247–267

    Article  Google Scholar 

  9. Yang C-T, Shih W-C, Cheng L-H (2012) Performance-based dynamic loop scheduling in heterogeneous computing environments. J Supercomput 59:414–442

    Article  Google Scholar 

  10. Ca B, Gb L (2002) Load balancing for heterogeneous clusters of pcs. Future Gener Comput Syst 18:389–400

    Article  Google Scholar 

  11. Yagoubi B, Slimani Y (2007) Load balancing strategy in grid environment. J Inf Technol Appl 1:285–296

    Google Scholar 

  12. Abdelkader DM, Omara F (2012) Dynamic task scheduling algorithm with load balancing for heterogeneous computing system. Egypt Inf J 13:135–145

    Article  Google Scholar 

  13. Yang C-T, Wu C-C, Chang J-H (2011) Performance-based parallel loop self-scheduling using hybrid openmp and mpi programming on multicore smp clusters. Concurr Comput Pract Exp 23:721–744

    Article  Google Scholar 

  14. Huang CW, Kuo CF, Yang CT, Liu JC, Chen ST (2015) Improvement of workload balancing using parallel loop self-scheduling on xeon phi. In: Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp 80–86

  15. Intel xeon phi (2014a) URL: http://www.intel.com.tw/content/www/tw/zh/processors/xeon/xeon-phi-detail.html

  16. Intel math kernel library-linpack (2014b) URL: https://software.intel.com/en-us/articles/intel-math-kernel-library-linpack-download

  17. Intel ark (2014c) URL: http://ark.intel.com

  18. Openmp wiki (2014d) URL: http://en.wikipedia.org/wiki/OpenMP

  19. Mpi wiki (2014e) URL: http://en.wikipedia.org/wiki/Message_Passing_Interface

  20. Open mpi (2014f) URL: http://www.open-mpi.org

  21. Yang C-T, Cheng K-W, Li K-C (2005) An enhanced parallel loop self-scheduling scheme for cluster environments. J Supercomput 34:315–335

    Article  Google Scholar 

  22. Yang C-T, Chang S-C (2004) A parallel loop self-scheduling on extremely heterogeneous pc clusters. J Inf Sci Eng 20:263–273

    Google Scholar 

  23. Yang C-T, Cheng K-W, Shih W-C (2007) On development of an efficient parallel loop self-scheduling for grid computing environments. Parallel Comput 33:467–487

    Article  Google Scholar 

  24. Han Y, Chronopoulos AT Scalable loop self-scheduling schemes implemented on large-scale clusters. In: IEEE International Symposium on Parallel and Distributed Processing, pp 1735–1742

  25. Sukhija N, Banicescu I, Ciorba FM (2015) Investigating the resilience of dynamic loop scheduling in heterogeneous computing systems. In: 14th International Symposium on Parallel and Distributed Computing, pp 194–203

  26. Carino RL, Banicescu I Dynamic scheduling parallel loops with variable iterate execution times. In: Proceedings 16th International Parallel and Distributed Processing Symposium, p 8

  27. Riakiotakis I, Papakonstantinou G, Chronopoulos AT (2008) Implementation of dynamic loop scheduling in reconfigurable platforms. In: 2008 International Symposium on Industrial Embedded Systems, pp 11–18

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Acknowledgements

This work was sponsored by the Ministry of Science and Technology, Taiwan ROC, under Grants Numbers MOST 104-2221-E-029-010-MY3, MOST 105-2634-E-029-001 and MOST 105-2622-E-029-003-CC3.

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  1. Department of Computer Science, Tunghai University, Taichung City, 40704, Taiwan, ROC

    Chao-Tung Yang, Chao-Wei Huang & Shuo-Tsung Chen

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  1. Chao-Tung Yang
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  2. Chao-Wei Huang
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Correspondence to Chao-Tung Yang.

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Yang, CT., Huang, CW. & Chen, ST. Improvement of workload balancing using parallel loop self-scheduling on Intel Xeon Phi. J Supercomput 73, 4981–5005 (2017). https://doi.org/10.1007/s11227-017-2068-9

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