Abstract
Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs for scheduling, resulting in two drawbacks. The first is inflexibility and inefficiency in processor allocation, leading to resource fragmentation and thus poor performance. The second is about usage inconvenience, requiring users to figure out the best number of processors for executing a job. As HPC as a service emerges, moldable job scheduling has become an important research issue for achieving both high performance and user convenience. This paper presents our research work on developing new processor allocation approaches for moldable job scheduling based on two-level resource utilization calculation, preemptive job execution, and dual-criteria iterative improvement. A series of simulation experiments have been conducted to evaluate the proposed approaches and compare them to previous methods. The experimental results demonstrate significant performance improvement in terms of average turnaround time.
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References
Feitelson DG, Rudolph L, Schweigelshohn U, Sevcik K, Wong P (1997) Theory and practice in parallel job scheduling. Lect Notes Comput Sci 1291:1–34
Kessler C, Melot N, Eitschberger P, Keller J (2013) Crown scheduling: energy-efficient resource allocation, mapping and discrete frequency scaling for collections of malleable streaming tasks. In: Proceedings of the 23rd international workshop on power and timing modeling, optimization and simulation
Wu X, Loiseau P (2015). Algorithms for scheduling deadline-sensitive malleable tasks. In: Proceedings of the fifty-third annual Allerton conference
Asghar S, Aubanel E, Bremner D (2013) A dynamic moldable job scheduling based parallel SAT solver. Proceedings of the international conference on parallel processing, pp 110–119
AbdelBaky M, Parashar M, Kim H, JordanKirk EJ, Sachdeva V, Sexton J, Jamjoom H, Shae ZY, Pencheva G, Tavakoli R, Wheeler MF (2012) Enabling high performance computing as a service. IEEE Comput 45:72–80
Bagga S, Garg D, Arora A (2014) Moldable load scheduling using demand adjustable policies. Proceedings of the international conference on advances in computing communications and informatics
Huang KC, Huang TC, Tsai MJ, Chang HY, Tung YH (2013) Moldable job scheduling for HPC as a service with application speedup model and execution time information. J Converg Sect A Comput Commun 4(4):14–22
Wu S, Tuo Q, Jin H, Yan C, Weng Q (2015) HRF: a resource allocation scheme for moldable jobs. In: Proceedings of the 12th ACM international conference on computing frontiers
Kwon OH, Kim J, Hong SJ, Lee SG (1997) Real-time job scheduling in hypercube systems. In: Proceedings of international conference on parallel processing, p 166
Kwon OH, Chwa KY (1998) An algorithm for scheduling jobs in hypercube systems. IEEE Trans Parallel Distrib Syst 9(9):856–860
Ni LM, Turner SW, Cheng BHC (1995) Contention-free 2D-mesh cluster allocation in hypercubes. IEEE Trans Comput 44(8):1051–1055
Sharma DD, Pradhan DK (1995) Processor allocation in hypercube multicomputers: fast and efficient strategies for cubic and noncubic allocation. IEEE Trans Parallel Distrib Syst 6(10):1108–1122
Mu’alem AW, Feitelson DG (2001) Utilization, predictability, workloads, and user runtime estimate in scheduling the IBM SP2 with backfilling. IEEE Trans Parallel Distrib Syst 12(6):529–543
Feitelson DG, Rudolph L (1995) Parallel job scheduling: issues and approaches. In: Proceedings of job scheduling strategies for parallel processing, pp 1–18
Lifka D (1995) The ANL/IBM SP scheduling system. In: Proceedings of the job scheduling strategies for parallel processing, pp 295–303
Skovira J, Chan W, Zhou H, Lifka D (1996) The EASY-LoadLeveler API project. In: Proceedings of the job scheduling strategies for parallel processing, pp 41–47
Feitelson, D.G., Weil, A.M. (1998). Utilization and predictability in scheduling the IBM SP2 with backfilling. In: Proceedings of the 12th Int’l parallel processing symposium, pp 542–546
Srinivasan S, Kettimuthu R, Subrarnani V, Sadayappan P (2002) Characterization of backfilling strategies for parallel job scheduling. In: Proceeding of the conference on parallel processing (ICPP), pp 514–522
Wong AKL, Goscinski AM (2007) Evaluating the EASY-backfill job scheduling of static workloads on clusters. In: Proceedings of the IEEE international conference in cluster computing, pp 64–73
Wong AK, Goscinski AM (2008) The impact of under-estimated length of jobs on EASY-backfill scheduling. In: Proceedings of the IEEE parallel, distributed and network-based processing
Feitelson DG (2005) Experimental analysis of the root causes of performance evaluation results: a backfilling case study. IEEE Trans Parallel Distrib Syst 16:175–182
Shmueli E, Feitelson DG (2005) Backfilling with lookahead to optimize the packing of parallel jobs. J Parallel Distrib Comput 65:1090–1107
Jackson DB, Snell Q, Clement MJ (2001) Core algorithms of the Maui scheduler. In: Proceedings of the job scheduling strategies for parallel processing
Feitelson DG, Rudolph L, Schwiegelshohn U (2005) Parallel job scheduling—a status report. In: Proceedings of the job scheduling strategies for parallel processing, pp 1–16
Tsafrir D, Etsion Y, Feitelson DG (2007) Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans Parallel Distrib Syst 18:789–803
Chiang SH, Vasupongayya S (2008) Design and potential performance of goal-oriented job scheduling policies for parallel computer workloads. IEEE Trans Parallel Distrib Syst 19:1642–1656
Stanzani S, Cóbe R, Fialho J, Iope R, Gomes M, Baruchi A, Amaral J (2019) Towards a strategy for performance prediction on heterogeneous architectures. In: Senger H et al (eds) High performance computing for computational science—VECPAR 2018. Lecture notes in computer science, vol 11333, pp 247–253
Shen C, Tong W, Choo KR et al (2018) Performance prediction of parallel computing models to analyze cloud-based big data applications. Cluster Comput 21:1439–1454
Martínez V, Serpa M, Dupros F, Padoin EL, Navaux P (2018). Performance prediction of acoustic wave numerical kernel on Intel Xeon Phi processor. In: Mocskos E, Nesmachnow S (eds) High performance computing. CARLA 2017. Communications in computer and information science, vol 796, pp 101–110
Caron E, Chouhan PK, Desprez F (2004) Deadline scheduling with priority for client-server systems on the grid. In: Proceedings of the fifth IEEE/ACM international workshop on grid computing
Le G, Xu K, Song J (2013) Dynamic resource provisioning and scheduling with deadline constraint in elastic cloud. In: Proceedings of the international conference on service science
Perret Q, Charlemagne G, Sotiriadis S, Bessis N (2013) A deadline scheduler for jobs in distributed systems. In: Proceedings of the 27th international conference on advanced information networking and applications workshops
Zhao W, Ramamritham K, Stankovic JA (1987) Scheduling tasks with resource requirements in hard real-time systems. IEEE Trans Softw Eng 13(5):564–577
Yoon H, Ryu M (2015) Guaranteeing end-to-end deadlines for AUTOSAR-based automotive software. Int J Automot Technol 16(4):635–644
Li J, Luo Z, Ferry D, Agrawal K, Gill C, Lu C (2014) Global EDF scheduling for parallel real-time tasks. Real Time Syst 51(4):395–439
Li J, Xiong M, Lee VCS, Shu L, Li G (2013) Workload efficient deadline and period assignment for maintaining temporal consistency under EDF. IEEE Trans Comput 62:1–14
Herrtwich RG (1990) An introduction to real-time scheduling. ICSI Technique report, TR-90-035
Pop F (2013) Scheduling of sporadic tasks with deadline constrains in cloud environments. In: Proceedings of the IEEE 27th international conference on advanced information networking and applications
Srinivasan S, Krishnamoorthy S, Sadayappan P (2003) A robust scheduling strategy for moldable scheduling of parallel jobs. In: Proceedings of the 5th IEEE international conference on cluster computing, pp 92–99
Srinivasan S, Subramani V, Kettimuthu R, Holenarsipur P, Sadayappan P (2002) Effective selection of partition sizes for moldable scheduling of parallel jobs. Lect Notes Comput Sci 2552:174–183
Cirne W, Berman F (2000) Adaptive selection of partition size for supercomputer requests. Lect Notes Comput Sci 1911:187–207
Cirne W, Berman F (2002) Using moldability to improve the performance of supercomputer jobs. J Parallel Distrib Comput 62:1571–1601
Sabin G, Lang M, Sadayappan P (2006) Moldable parallel job scheduling using job efficiency: an iterative approach. In: Proceedings of the job scheduling strategies for parallel processing, Saint Malo, France
Caniou Y, Charrier G, Desprez F (2011) Evaluation of reallocation heuristics for moldable tasks in computational grids. In: Proceedings of the 9th Australasian symposium on parallel and distributed computing
Huang KC (2006) Performance evaluation of adaptive processor allocation policies for moldable parallel batch jobs. In: Proceedings of the 3th workshop on grid technologies and applications
Beheshti Roui M, Shekofteh SK, Noori H et al (2020) Efficient scheduling of streams on GPGPUs. J Supercomput. https://doi.org/10.1007/s11227-020-03209-x
Chen Q, Oh J, Kim S et al (2019) Design of an adaptive GPU sharing and scheduling scheme in container-based cluster. Cluster Comput. https://doi.org/10.1007/s10586-019-02969-3
Choi HJ, Son DO, Kang SG et al (2013) An efficient scheduling scheme using estimated execution time for heterogeneous computing systems. J Supercomput 65:886–902
Chen L, Ye D, Zhang G (2013). Online scheduling on a CPU–GPU cluster. In: Chan TH, Lau LC, Trevisan L (eds) Theory and applications of models of computation, TAMC 2013, Lecture notes in computer science, vol 7876, pp 1–9
Zhou Z, Li F, Zhu H et al (2019) An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04119-7
Mortazavi-Dehkordi M, Zamanifar K (2020) Efficient deadline-aware scheduling for the analysis of Big Data streams in public Cloud. Cluster Comput 23:241–263
Baskiyar S, Abdel-Kader R (2010) Energy aware DAG scheduling on heterogeneous systems. Cluster Comput 13:373–383
Mei J, Li K, Li K (2014) Energy-aware task scheduling in heterogeneous computing environments. Cluster Comput 17:537–550
Maurya AK, Modi K, Kumar V et al (2019) Energy-aware scheduling using slack reclamation for cluster systems. Cluster Comput. https://doi.org/10.1007/s10586-019-02965-7
Kleinrock L, Huang JH (1992) On parallel processing systems: Amdahl’s law generalized and some results on optimal design. IEEE Trans Softw Eng 18(5):434–447
Downey AB (1997a) A model for speedup of parallel programs. UC Berkeley EECS Technical Report No. UCB/CSD-97-933
Downey AB (1997b) A parallel workload model and its implications for processor allocation. In: Proceedings of the 6th international symposium on high performance distributed computing
Lewis TG, Rewini HE (1992) Introduction to parallel computing. Prentice-Hall International, Upper Saddle River
Radulescu A, Nicolescu C, van Gemund AJC, Jonker PP (2001) CPR: mixed task and data parallel scheduling for distributed systems. In: Proceedings of the 15th international parallel and distributed processing symposium
Memeti S, Pllana S, Binotto A et al (2019) Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing 101:893–936
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Wu, YJ., Yu, ST., Lai, KC. et al. Two-level utilization-based processor allocation for scheduling moldable jobs. J Supercomput 76, 10212–10239 (2020). https://doi.org/10.1007/s11227-020-03246-6
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DOI: https://doi.org/10.1007/s11227-020-03246-6