Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions

  • Review Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Nowadays, high-performance computing (HPC) clusters are increasingly popular. Large volumes of job logs recording many years of operation traces have been accumulated. In the same time, the HPC cloud makes it possible to access HPC services remotely. For executing applications, both HPC end-users and cloud users need to request specific resources for different workloads by themselves. As users are usually not familiar with the hardware details and software layers, as well as the performance behavior of the underlying HPC systems. It is hard for them to select optimal resource configurations in terms of performance, cost, and energy efficiency. Hence, how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community. Prediction of job characteristics plays a key role for intelligent resource allocation. This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems. We first review the existing techniques in obtaining performance and energy consumption data of jobs. Then we survey the techniques for single-objective oriented predictions on runtime, queue time, power and energy consumption, cost and optimal resource configuration for input jobs, as well as multi-objective oriented predictions. We conclude after discussing future trends, research challenges and possible solutions towards intelligent resource allocation in HPC systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Feitelson D G, Tsafrir D, Krakov D. Experience with using the parallel workloads archive. Journal of Parallel and Distributed Computing, 2014, 74(10): 2967–2982

    Article  Google Scholar 

  2. Wallace S, Yang X, Vishwanath V, Allcock W E, Coghlan S, Papka M E, Lan Z. A data driven scheduling approach for power management on HPC systems. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2016, 56

  3. Tsujita Y, Uno A, Sekizaw R, Yamamoto K, Sueyasu F. Job classification through long-term log analysis towards power-aware HPC system operation. In: Proceedings of the 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). 2021, 26–34

  4. Fan Y, Rich P, Allcock W E, Papka M E, Lan Z. Trade-off between prediction accuracy and underestimation rate in job runtime estimates. In: Proceedings of the 2017 IEEE International Conference on Cluster Computing (CLUSTER). 2017, 530–540

  5. Netto M A S, Calheiros R N, Rodrigues E R, Cunha R L F, Buyya R. HPC cloud for scientific and business applications: taxonomy, vision, and research challenges. ACM Computing Surveys, 2019, 51(1): 8

    Article  Google Scholar 

  6. Mariani G, Anghel A, Jongerius R, Dittmann G. Predicting cloud performance for HPC applications before deployment. Future Generation Computer Systems, 2018, 87: 618–628

    Article  Google Scholar 

  7. Orgerie A C, De Assuncao M D, Lefevre L. A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Computing Surveys, 2014, 46(4): 47

    Article  Google Scholar 

  8. Kelechi A H, Alsharif M H, Bameyi O J, Ezra P J, Joseph I K, Atayero A A, Geem Z W, Hong J. Artificial intelligence: an energy efficiency tool for enhanced high performance computing. Symmetry, 2020, 12(6): 1029

    Article  Google Scholar 

  9. Wang E D. High Productivity Computing System: Design and Applications. China Science Publishing & Media Ltd, 2014

  10. Prabhakaran S. Dynamic resource management and job scheduling for high performance computing. Technische Universität Darmstadt, Dissertation, 2016

  11. Ge R, Cameron K W. Power-aware speedup. In: Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium. 2007, 1–10

  12. Cunha R L F, Rodrigues E R, Tizzei L P, Netto M A S. Job placement advisor based on turnaround predictions for HPC hybrid clouds. Future Generation Computer Systems, 2017, 67: 35–46

    Article  Google Scholar 

  13. Leite A F, Boukerche A, De Melo A C M A, Eisenbeis C, Tadonki C, Ralha C G. Power-aware server consolidation for federated clouds. Concurrency and Computation: Practice and Experience, 2016, 28(12): 3427–3444

    Article  Google Scholar 

  14. Yu L, Zhou Z, Fan Y, Papka M E, Lan Z. System-wide trade-off modeling of performance, power, and resilience on petascale systems. The Journal of Supercomputing, 2018, 74(7): 3168–3192

    Article  Google Scholar 

  15. Blagodurov S, Fedorova A, Vinnik E, Dwyer T, Hermenier F. Multi-objective job placement in clusters. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2015, 66

  16. Toosi A N, Calheiros R N, Buyya R. Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Computing Surveys, 2014, 47(1): 7

    Article  Google Scholar 

  17. Hou Z, Wang Y, Sui Y, Gu J, Zhao T, Zhou X. Managing highperformance computing applications as an on-demand service on federated clouds. Computers & Electrical Engineering, 2018, 67: 579–595

    Article  Google Scholar 

  18. Hussain H, Malik S U R, Hameed A, Khan S U, Bickler G, Min-Allah N, Qureshi M B, Zhang L, Wang Y, Ghani N, Kolodziej J, Zomaya A Y, Xu C Z, Balaji P, Vishnu A, Pinel F, Pecero J E, Kliazovich D, Bouvry P, Li H, Wang L, Chen D, Rayes A. A survey on resource allocation in high performance distributed computing systems. Parallel Computing, 2013, 39(11): 709–736

    Article  MathSciNet  Google Scholar 

  19. Massie M L, Chun B N, Culler D E. The ganglia distributed monitoring system: design, implementation, and experience. Parallel Computing, 2004, 30(7): 817–840

    Article  Google Scholar 

  20. Allcock W, Rich P, Fan Y, Lan Z. Experience and practice of batch scheduling on leadership supercomputers at Argonne. In: Proceedings of 21st Job Scheduling Strategies for Parallel Processing. 2017, 1–24

  21. Yoon J, Hong T, Park C, Noh S Y, Yu H. Log analysis-based resource and execution time improvement in HPC: a case study. Applied Sciences, 2020, 10(7): 2634

    Article  Google Scholar 

  22. Islam S, Keung J, Lee K, Liu A. Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 2012, 28(1): 155–162

    Article  Google Scholar 

  23. Cortez E, Bonde A, Muzio A, Russinovich M, Fontoura M, Bianchini R. Resource central: understanding and predicting workloads for improved resource management in large cloud platforms. In: Proceedings of the 26th Symposium on Operating Systems Principles. 2017, 153–167

  24. Marowka A. On performance analysis of a multithreaded application parallelized by different programming models using Intel VTune. In: Proceedings of the 11th International Conference on Parallel Computing Technologies. 2011, 317–331

  25. Terpstra D, Jagode H, You H, Dongarra J. Collecting performance data with PAPI-C. In: Proceedings of the 3rd International Workshop on Parallel Tools for High Performance Computing. 2009, 157–173

  26. Dimakopoulou M, Eranian S, Koziris N, Bambos N. Reliable and efficient performance monitoring in Linux. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2016, 396–408

  27. Weaver V M. Self-monitoring Overhead of the Linux perf_event performance counter interface. In: Proceedings of the 2015 IEEE International Symposium on Performance Analysis of Systems and Software. 2015, 102–111

  28. Treibig J, Hager G, Wellein G. LIKWID: a lightweight performance-oriented tool suite for x86 multicore environments. In: Proceedings of the 39th International Conference on Parallel Processing Workshops. 2010, 207–216

  29. Pospiech C. Hardware performance monitor (HPM) toolkit users guide. Advanced Computing Technology Center, IBM Research. See researcher.watson.ibm.com/researcher/files/us-hfwen/HPM_ug.pdf website, 2008

  30. Georgiou Y, Glesser D, Rzadca K, Trystram D. A scheduler-level incentive mechanism for energy efficiency in HPC. In: Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2015, 617–626

  31. Raghu H V, Saurav S K, Bapu B S. PAAS: power aware algorithm for scheduling in high performance computing. In: Proceedings of the 6th IEEE/ACM International Conference on Utility and Cloud Computing. 2013, 327–332

  32. Wallace S, Vishwanath V, Coghlan S, Tramm J, Lan Z, Papka M E. Application power profiling on IBM Blue Gene/Q. In: Proceedings of the 2013 IEEE International Conference on Cluster Computing (CLUSTER). 2013, 1–8

  33. Browne S, Dongarra J, Garner N, Ho G, Mucci P. A portable programming interface for performance evaluation on modern processors. The International Journal of High Performance Computing Applications, 2000, 14(3): 189–204

    Article  Google Scholar 

  34. Rashti M, Sabin G, Vansickle D, Norris B. WattProf: a flexible platform for fine-grained HPC power profiling. In: Proceedings of the 2015 IEEE International Conference on Cluster Computing. 2015, 698–705

  35. Laros J H, DeBonis D, Grant R E, Kelly S M, Levenhagen M, Olivier S, Pedretti K. High performance computing-power application programming interface specification, version 1.2. See cfwebprod. sandia.gov/cfdocs/CompResearch/docs/PowerAPI_SAND_V1.1a(3). pdf website, 2016

  36. Kavanagh R, Djemame K. Rapid and accurate energy models through calibration with IPMI and RAPL. Concurrency and Computation: Practice and Experience, 2019, 31(13): e5124

    Article  Google Scholar 

  37. Weaver V M, Johnson M, Kasichayanula K, Ralph J, Luszczek P, Terpstra D, Moore S. Measuring energy and power with PAPI. In: Proceedings of the 41st International Conference on Parallel Processing Workshops. 2012, 262–268

  38. Rotem E, Naveh A, Ananthakrishnan A, Weissmann E, Rajwan D. Power-management architecture of the Intel microarchitecture code-named Sandy Bridge. IEEE Micro, 2012, 32(2): 20–27

    Article  Google Scholar 

  39. Leng J, Hetherington T, ElTantawy A, Gilani S, Kim N S, Aamodt T M, Reddi V J. GPUwattch: enabling energy optimizations in GPGPUs. In: Proceedings of the 40th Annual International Symposium on Computer Architecture. 2013, 487–498

  40. Saillant T, Weill J C, Mougeot M. Predicting job power consumption based on RJMS submission data in HPC systems. In: Proceedings of the 35th International Conference on High Performance Computing. 2020, 63–82

  41. Jin C, De Supinski B R, Abramson D, Poxon H, DeRose L, Dinh M N, Endrei M, Jessup E R. A survey on software methods to improve the energy efficiency of parallel computing. The International Journal of High Performance Computing Applications, 2017, 31(6): 517–549

    Article  Google Scholar 

  42. Georgiou Y, Cadeau T, Glesser D, Auble D, Jette M, Hautreux M. Energy accounting and control with SLURM resource and job management system. In: Proceedings of the 15th International Conference on Distributed Computing and Networking. 2014, 96–118

  43. Martin S J, Rush D, Kappel M. Cray advanced platform monitoring and control. In: Proceedings of the Cray User Group Meeting, Chicago, IL. See cug.org/proceedings/cug2015_proceedings/includes/files/pap132-file2.pdf website, 2015, 26–30

  44. Thain D, Tannenbaum T, Livny M. Distributed computing in practice: the Condor experience. Concurrency and Computation: Practice and Experience, 2005, 17(2–4): 323–356

    Article  Google Scholar 

  45. Yoo A B, Jette M A, Grondona M. SLURM: simple Linux utility for resource management. In: Proceedings of the 9th Workshop on Job Scheduling Strategies for Parallel Processing. 2003, 44–60

  46. Gibbons R. A historical application profiler for use by parallel schedulers. In: Proceedings of Workshop on Job Scheduling Strategies for Parallel Processing. 1997, 58–77

  47. Smith W, Foster I, Taylor V. Predicting application run times with historical information. Journal of Parallel and Distributed Computing, 2004, 64(9): 1007–1016

    Article  MATH  Google Scholar 

  48. Schopf J M, Berman F. Using stochastic intervals to predict application behavior on contended resources. In: Proceedings of the Fourth International Symposium on Parallel Architectures, Algorithms, and Networks. 1999, 344–349

  49. Mendes C L, Reed D A. Integrated compilation and scalability analysis for parallel systems. In: Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques. 1998, 385–392

  50. Nissimov A. Locality and its usage in parallel job runtime distribution modeling using HMM. Hebrew University, Dissertation, 2006

  51. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, 77(2): 257–286

    Article  Google Scholar 

  52. Tsafrir D, Etsion Y, Feitelson D G. Backfilling using system-generated predictions rather than user runtime estimates. IEEE Transactions on Parallel and Distributed Systems, 2007, 18(6): 789–803

    Article  Google Scholar 

  53. Hou Z, Zhao S, Yin C, Wang Y, Gu J, Zhou X. Machine learning based performance analysis and prediction of jobs on a HPC cluster. In: Proceedings of the 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). 2019, 247–252

  54. Matsunaga A, Fortes J A B. On the use of machine learning to predict the time and resources consumed by applications. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. 2010, 495–504

  55. Duan R, Nadeem F, Wang J, Zhang Y, Prodan R, Fahringer T. A hybrid intelligent method for performance modeling and prediction of workflow activities in grids. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. 2009, 339–347

  56. Gaussier E, Glesser D, Reis V, Trystram D. Improving backfilling by using machine learning to predict running times. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2015, 1–10

  57. Li J, Zhang X, Han L, Ji Z, Dong X, Hu C. OKCM: improving parallel task scheduling in high-performance computing systems using online learning. The Journal of Supercomputing, 2021, 77(6): 5960–5983

    Article  Google Scholar 

  58. McGough A S, Moubayed N A, Forshaw M. Using machine learning in trace-driven energy-aware simulations of high-throughput computing systems. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion. 2017, 55–60

  59. Chen X, Zhang H, Bai H, Yang C, Zhao X, Li B. Runtime prediction of high-performance computing jobs based on ensemble learning. In: Proceedings of the 4th International Conference on High Performance Compilation, Computing and Communications. 2020, 56–62

  60. Wu G B, Shen Y, Zhang W S, Liao S S, Wang Q Q, Li J. Runtime prediction of jobs for backfilling optimization. Journal of Chinese Computer Systems (in Chinese), 2019, 40(1): 6–12

    Google Scholar 

  61. Xiao Y H, Xu L F, Xiong M. GA-Sim: a job running time prediction algorithm based on categorization and instance learning. Computer Engineering & Science (in Chinese), 2019, 41(6): 987–992

    Google Scholar 

  62. Parashar M, AbdelBaky M, Rodero I, Devarakonda A. Cloud paradigms and practices for computational and data-enabled science and engineering. Computing in Science & Engineering, 2013, 15(4): 10–18

    Article  Google Scholar 

  63. Li X, Palit H, Foo Y S, Hung T. Building an HPC-as-a-service toolkit for user-interactive HPC services in the cloud. In: Proceedings of the 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications. 2011, 369–374

  64. Shi J Y, Taifi M, Pradeep A, Khreishah A, Antony V. Program scalability analysis for HPC cloud: applying Amdahl’s law to NAS benchmarks. In: Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis. 2012, 1215–1225

  65. Saad A, El-Mahdy A. HPCCloud seer: a performance model based predictor for parallel applications on the cloud. IEEE Access, 2020, 8: 87978–87993

    Article  Google Scholar 

  66. Fan C T, Chang Y S, Wang W J, Yuan S M. Execution time prediction using rough set theory in hybrid cloud. In: Proceedings of the 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing. 2012, 729–734

  67. Smith W, Taylor V E, Foster I T. Using run-time predictions to estimate queue wait times and improve scheduler performance. In: Proceedings of the Job Scheduling Strategies for Parallel Processing. 1999, 202–219

  68. Nurmi D, Brevik J, Wolski R. QBETS: queue bounds estimation from time series. In: Proceedings of the 13th Workshop on Job Scheduling Strategies for Parallel Processing. 2007, 76–101

  69. Brevik J, Nurmi D, Wolski R. Predicting bounds on queuing delay for batch-scheduled parallel machines. In: Proceedings of the 11th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 2006, 110–118

  70. Nurmi D, Mandal A, Brevik J, Koelbel C, Wolski R, Kennedy K. Evaluation of a workflow scheduler using integrated performance modelling and batch queue wait time prediction. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing. 2006, 29

  71. Netto M A S, Cunha R L F, Sultanum N. Deciding when and how to move HPC jobs to the cloud. Computer, 2015, 48(11): 86–89

    Article  Google Scholar 

  72. Smith W. A service for queue prediction and job statistics. In: Proceedings of the 2010 Gateway Computing Environments Workshop (GCE). 2010, 1–8

  73. Murali P, Vadhiyar S. Qespera: an adaptive framework for prediction of queue waiting times in supercomputer systems. Concurrency and Computation: Practice and Experience, 2016, 28(9): 2685–2710

    Article  Google Scholar 

  74. Murali P, Vadhiyar S. Metascheduling of HPC jobs in day-ahead electricity markets. IEEE Transactions on Parallel and Distributed Systems, 2018, 29(3): 614–627

    Article  Google Scholar 

  75. Elnozahy E N, Kistler M, Rajamony R. Energy-efficient server clusters. In: Proceedings of the 2nd International Workshop on Power-aware Computer Systems. 2002, 179–197

  76. Lawson B, Smirni E. Power-aware resource allocation in high-end systems via online simulation. In: Proceedings of the 19th Annual International Conference on Supercomputing. 2005, 229–238

  77. Etinski M, Corbalan J, Labarta J, Valero M. Optimizing job performance under a given power constraint in HPC centers. In: Proceedings of the International Conference on Green Computing. 2010, 257–267

  78. Etinski M, Corbalan J, Labarta J, Valero M. Parallel job scheduling for power constrained HPC systems. Parallel Computing, 2012, 38(12): 615–630

    Article  MathSciNet  Google Scholar 

  79. Mämmelä O, Majanen M, Basmadjian R, De Meer H, Giesler A, Homberg W. Energy-aware job scheduler for high-performance computing. Computer Science — Research and Development, 2012, 27(4): 265–275

    Article  Google Scholar 

  80. Zhou Z, Lan Z, Tang W, Desai N. Reducing energy costs for IBM Blue Gene/P via power-aware job scheduling. In: Proceedings of the 17th Workshop on Job Scheduling Strategies for Parallel Processing. 2014, 96–115

  81. Marathe A, Bailey P E, Lowenthal D K, Rountree B, Schulz M, De Supinski B R. A run-time system for power-constrained HPC applications. In: Proceedings of the 30th International Conference on High Performance Computing. 2015, 394–408

  82. Dhiman G, Mihic K, Rosing T. A system for online power prediction in virtualized environments using gaussian mixture models. In: Proceedings of the 47th Design Automation Conference. 2010, 807–812

  83. Basmadjian R, De Meer H. Evaluating and modeling power consumption of multi-core processors. In: Proceedings of the 3rd International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy). 2012, 1–10

  84. Basmadjian R, Costa G D, Chetsa G L T, Lefevre L, Oleksiak A, Pierson J M. Energy-aware approaches for HPC systems. In: Jeannot E, Žilinskas J, eds. High-Performance Computing on Complex Environments. Hoboken: John Wiley & Sons, Inc, 2014

    Google Scholar 

  85. Subramaniam B, Feng W C. Statistical power and performance modeling for optimizing the energy efficiency of scientific computing. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing. 2010, 139–146

  86. John L K, Eeckhout L. Performance Evaluation and Benchmarking. New York: CRC Press, 2005

    Google Scholar 

  87. Patki T, Lowenthal D K, Rountree B, Schulz M, De Supinski B R. Exploring hardware overprovisioning in power-constrained, high performance computing. In: Proceedings of the 27th International ACM Conference on International Conference on Supercomputing. 2013, 173–182

  88. Patki T, Lowenthal D K, Sasidharan A, Maiterth M, Rountree B L, Schulz M, De Supinski B R. Practical resource management in power-constrained, high performance computing. In: Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing. 2015, 121–132

  89. Sarood O, Langer A, Gupta A, Kale L. Maximizing throughput of overprovisioned HPC data centers under a strict power budget. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2014, 807–818

  90. Ellsworth D A, Malony A D, Rountree B, Schulz M. Dynamic power sharing for higher job throughput. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2015, 80

  91. Chiesi M, Vanzolini L, Mucci C, Scarselli E F, Guerrieri R. Power-aware job scheduling on heterogeneous multicore architectures. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(3): 868–877

    Article  Google Scholar 

  92. Sîrbu A, Babaoglu O. Power consumption modeling and prediction in a hybrid CPU-GPU-MIC supercomputer. In: Proceedings of the 22nd European Conference on Parallel Processing. 2016, 117–130

  93. Ciznicki M, Kurowski K, Weglarz J. Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures. Cluster Computing, 2017, 20(3): 2535–2549

    Article  Google Scholar 

  94. Dayarathna M, Wen Y, Fan R. Data center energy consumption modeling: a survey. IEEE Communications Surveys & Tutorials, 2016, 18(1): 732–794

    Article  Google Scholar 

  95. Lee E K, Viswanathan H, Pompili D. VMAP: proactive thermal-aware virtual machine allocation in HPC cloud datacenters. In: Proceedings of the 19th International Conference on High Performance Computing. 2012, 1–10

  96. Aversa R, Di Martino B, Rak M, Venticinque S, Villano U. Performance prediction for HPC on clouds. In: Buyya R, Broberg J, Goscinski A, eds. Cloud Computing: Principles and Paradigms. Hoboken: John Wiley & Sons, Inc, 2011

    Google Scholar 

  97. Liu M, Jin Y, Zhai J, Zha Y, Shi Q, Ma X, Chen W. ACIC: automatic cloud I/O configurator for HPC applications. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. 2013, 1–12

  98. Rak M, Turtur M, Villano U. Early prediction of the cost of cloud usage for HPC applications. Scalable Computing: Practice and Experience, 2015, 16(3): 303–320

    Google Scholar 

  99. Geist A, Reed D A. A survey of high-performance computing scaling challenges. The International Journal of High Performance Computing Applications, 2017, 31(1): 104–113

    Article  Google Scholar 

  100. Wang Z, O’Boyle M F P. Mapping parallelism to multi-cores: a machine learning based approach. In: Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 2009, 75–84

  101. Cochran R, Hankendi C, Coskun A, Reda S. Identifying the optimal energy-efficient operating points of parallel workloads. In: Proceedings of the 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 2011, 608–615

  102. Gomatheeshwari B, Selvakumar J. Appropriate allocation of workloads on performance asymmetric multicore architectures via deep learning algorithms. Microprocessors and Microsystems, 2020, 73: 102996

    Article  Google Scholar 

  103. Bai X, Wang E, Dong X, Zhang X. A scalability prediction approach for multi-threaded applications on manycore processors. The Journal of Supercomputing, 2015, 71(11): 4072–4094

    Article  Google Scholar 

  104. Ju T, Wu W, Chen H, Zhu Z, Dong X. Thread count prediction model: dynamically adjusting threads for heterogeneous many-core systems. In: Proceedings of the 21st IEEE International Conference on Parallel and Distributed Systems. 2015, 456–464

  105. Lawson G, Sundriyal V, Sosonkina M, Shen Y. Modeling performance and energy for applications offloaded to Intel Xeon Phi. In: Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing. 2015, 7

  106. Ozer G, Garg S, Davoudi N, Poerwawinata G, Maiterth M, Netti A, Tafani D. Towards a predictive energy model for HPC runtime systems using supervised learning. In: Proceedings of the European Conference on Parallel Processing. 2019, 626–638

  107. Niu S, Zhai J, Ma X, Tang X, Chen W, Zheng W. Building semi-elastic virtual clusters for cost-effective HPC cloud resource provisioning. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(7): 1915–1928

    Article  Google Scholar 

  108. Balaprakash P, Tiwari A, Wild S M, Carrington L, Hovland P D. AutoMOMML: automatic multi-objective modeling with machine learning. In: Proceedings of the 31st International Conference on High Performance Computing. 2016, 219–239

  109. Curtis-Maury M, Blagojevic F, Antonopoulos C D, Nikolopoulos D S. Prediction-based power-performance adaptation of multithreaded scientific codes. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(10): 1396–1410

    Article  Google Scholar 

  110. De Sensi D. Predicting performance and power consumption of parallel applications. In: Proceedings of the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). 2016, 200–207

  111. Endrei M, Jin C, Dinh M N, Abramson D, Poxon H, DeRose L, De Supinski B R. Energy efficiency modeling of parallel applications. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2018, 212–224

  112. Manumachu R R, Lastovetsky A. Bi-objective optimization of dataparallel applications on homogeneous multicore clusters for performance and energy. IEEE Transactions on Computers, 2018, 67(2): 160–177

    Article  MathSciNet  MATH  Google Scholar 

  113. Hao M, Zhang W, Wang Y, Lu G, Wang F, Vasilakos A V. Finegrained powercap allocation for power-constrained systems based on multi-objective machine learning. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(7): 1789–1801

    Google Scholar 

  114. Scogland T, Azose J, Rohr D, Rivoire S, Bates N, Hackenberg D. Node Variability in Large-Scale Power Measurements: perspectives from the Green500, Top500 and EEHPCWG. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2015, 1–11

  115. Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. In: Proceedings of the 2008 Grid Computing Environments Workshop. 2008, 1–10

  116. Seneviratne S, Witharana S. A survey on methodologies for runtime prediction on grid environments. In: Proceedings of the 7th International Conference on Information and Automation for Sustainability. 2014, 1–6

  117. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 12

    Article  Google Scholar 

  118. Ben-Nun T, Hoefler T. Demystifying parallel and distributed deep learning: an in-depth concurrency analysis. ACM Computing Surveys, 2020, 52(4): 65

    Article  Google Scholar 

  119. Li C, Sun H, Tang H, Luo Y. Adaptive resource allocation based on the billing granularity in edge-cloud architecture. Computer Communications, 2019, 145: 29–42

    Article  Google Scholar 

  120. Orhean A I, Pop F, Raicu I. New scheduling approach using reinforcement learning for heterogeneous distributed systems. Journal of Parallel and Distributed Computing, 2018, 117: 292–302

    Article  Google Scholar 

  121. Chen C L P, Liu Z. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10–24

    Article  MathSciNet  Google Scholar 

  122. Naghshnejad M, Singhal M. A hybrid scheduling platform: a runtime prediction reliability aware scheduling platform to improve HPC scheduling performance. The Journal of Supercomputing, 2020, 76(1): 122–149

    Article  Google Scholar 

  123. Ye D, Chen D Z, Zhang G. Online scheduling of moldable parallel tasks. Journal of Scheduling, 2018, 21(6): 647–654

    Article  MathSciNet  MATH  Google Scholar 

  124. Dongarra J J, Simon H D. High performance computing in the US in 1995 — An analysis on the basis of the TOP500 list. Supercomputer, 1997, 13(1): 19–28

    Google Scholar 

  125. Feng W C, Cameron K W. The Green500 list: encouraging sustainable supercomputing. Computer, 2007, 40(12): 50–55

    Article  Google Scholar 

  126. Wienke S, Iliev H, Mey D A, Muller M S. Modeling the productivity of HPC systems on a computing center scale. In: Proceedings of the 30th International Conference on High Performance Computing. 2015, 358–375

  127. Dongarra J, Graybill R, Harrod W, Lucas R, Lusk E, Luszczek P, Mcmahon J, Snavely A, Vetter J, Yelick K, Alam S, Campbell R, Carrington L, Chen T Y, Khalili O, Meredith J, Tikir M. DARPA’s HPCS program: history, models, tools, languages. Advances in Computers, 2008, 72: 1–100

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and suggestions. We also thank Dr./Prof. Feng Zhang from Renmin University of China and Dr./Prof. Jidong Zhai from Tsinghua University, China for their helpful suggestions and discussions. This work was partly supported by the National Key R&D Program of China (2018YFB0204100), the Science & Technology Innovation Project of Shaanxi Province (2019ZDLGY17-02), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. Center for High Performance Computing, School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhengxiong Hou, Xingshe Zhou, Jianhua Gu, Yunlan Wang & Tianhai Zhao

  2. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Hong Shen

Authors
  1. Zhengxiong Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xingshe Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianhua Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunlan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianhai Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengxiong Hou.

Additional information

Zhengxiong Hou received the PhD degree in computer science and technology from Northwestern Polytechnical University, China. He is an associate professor at the Center for High-Performance Computing, School of Computer Science, Northwestern Polytechnical University, China. His research interests include intelligent resource management and job scheduling, performance optimization in HPC clusters and clouds.

Hong Shen received the BEng degree from the Beijing University of Science and Technology, the MEng degree from the University of Science and Technology of China, the PhLic and PhD degrees from Abo Akademi University, Finland, all in computer science. He is currently a specially-appointed professor at Sun Yat-Sen University, China. He was a professor (Chair) of computer science in the University of Adelaide, Australia. With main research interests in parallel and distributed computing, algorithms, and high performance networks, he has published more than 300 papers including more than 100 papers in international journals such as a variety of IEEE and ACM transactions.

Xingshe Zhou received the BS and MS degrees in computer science from Northwestern Polytechnical University, China. He is a professor with the School of Computer Science, Northwestern Polytechnical University, China. He was the dean and director of the Center for High-Performance Computing of this university. His research interests include embedded computing and distributed computing. He has published more than 100 papers in international journals and conferences.

Jianhua Gu received the PhD degree in computer science and engineering from Northwestern Polytechnical University, China. He is a professor at the Center for High-Performance Computing, School of Computer Science, Northwestern Polytechnical University, China. His research interests include operating system and cloud computing.

Yunlan Wang received the PhD degree in computer science from Xi’an Jiaotong University, China. She is an associate professor at the Center for High-Performance Computing, School of Computer Science, Northwestern Polytechnical University, China. Her research interests include high-performance computing and data mining.

Tianhai Zhao received the PhD degree in computer science from Xi’an Jiaotong University, China. He is a lecturer at the Center for High-Performance Computing, School of Computer Science, Northwestern Polytechnical University, China. His research interests include parallel computing and cloud computing.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, Z., Shen, H., Zhou, X. et al. Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions. Front. Comput. Sci. 16, 165107 (2022). https://doi.org/10.1007/s11704-022-0625-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-022-0625-8

Keywords

Search

Navigation