CLC number: TP338.6
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-10-25
Cited: 0
Clicked: 6355
Wei Hu, Guang-ming Liu, Qiong Li, Yan-huang Jiang, Gui-lin Cai. Storage wall for exascale supercomputing[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1154-1175.
@article{title="Storage wall for exascale supercomputing",
author="Wei Hu, Guang-ming Liu, Qiong Li, Yan-huang Jiang, Gui-lin Cai",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="11",
pages="1154-1175",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601336"
}
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T1 - Storage wall for exascale supercomputing
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J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601336
Abstract: The mismatch between compute performance and I/O performance has long been a stumbling block as supercomputers evolve from petaflops to exaflops. Currently, many parallel applications are I/O intensive, and their overall running times are typically limited by I/O performance. To quantify the I/O performance bottleneck and highlight the significance of achieving scalable performance in peta/exascale supercomputing, in this paper, we introduce for the first time a formal definition of the ‘storage wall’ from the perspective of parallel application scalability. We quantify the effects of the storage bottleneck by providing a storage-bounded speedup, defining the storage wall quantitatively, presenting existence theorems for the storage wall, and classifying the system architectures depending on I/O performance variation. We analyze and extrapolate the existence of the storage wall by experiments on Tianhe-1A and case studies on Jaguar. These results provide insights on how to alleviate the storage wall bottleneck in system design and achieve hardware/software optimizations in peta/exascale supercomputing.
[1]Agarwal, S., Garg, R., Gupta, M.S., et al., 2004. Adaptive incremental checkpointing for massively parallel systems. Proc. 18th Annual Int. Conf. on Supercomputing, p.277-286.
[2]Agerwala, T., 2010. Exascale computing: the challenges and opportunities in the next decade. IEEE 16th Int. Symp. on High Performance Computer Architecture.
[3]Alam, S.R., Kuehn, J.A., Barrett, R.F., et al., 2007. Cray XT4: an early evaluation for petascale scientific simulation. Proc. ACM/IEEE Conf. on Supercomputing, p.1-12.
[4]Ali, N., Carns, P.H., Iskra, K., et al., 2009. Scalable I/O forwarding framework for high-performance computing systems. IEEE Int. Conf. on Cluster Computing and Workshops, p.1-10,
[5]Amdahl, G.M., 1967. Validity of the single processor approach to achieving large scale computing capabilities. Proc. Spring Joint Computer Conf., p.483-485.
[6]Bent, J., Gibson, G., Grider, G., et al., 2009. PLFS: a checkpoint file system for parallel applications. Proc. Conf. on High Performance Computing Networking, Storage and Analysis, p.21.
[7]Cappello, F., Geist, A., Gropp, B., et al., 2009. Toward exascale resilience. Int. J. High Perform. Comput. Appl., 23(4):374-388.
[8]Carns, P., Harms, K., Allcock, W., et al., 2011. Understanding and improving computational science storage access through continuous characterization. ACM Trans. Stor., 7(3):1-26.
[9]Chen, J., Tang, Y.H., Dong, Y., et al., 2016. Reducing static energy in supercomputer interconnection networks using topology-aware partitioning. IEEE Trans. Comput., 65(8):2588-2602.
[10]Culler, D.E., Singh, J.P., Gupta, A., 1998. Parallel Computer Architecture: a Hardware/Software Approach. Morgan Kaufmann Publishers Inc., San Francisco, USA.
[11]Egwutuoha, I.P., Levy, D., Selic, B., et al., 2013. A survey of fault tolerance mechanisms and checkpoint/restart implementations for high performance computing systems. J. Supercomput., 65(3):1302-1326.
[12]Elnozahy, E.N., Plank, J.S., 2004. Checkpointing for peta-scale systems: a look into the future of practical rollback-recovery. IEEE Trans. Depend. Secur. Comput., 1(2):97-108.
[13]Elnozahy, E.N., Alvisi, L., Wang, Y.M., et al., 2002. A survey of rollback-recovery protocols in message-passing systems. ACM Comput. Surv., 34(3):375-408.
[14]Fahey, M., Larkin, J., Adams, J., 2008. I/O performance on a massively parallel cray XT3/XT4. IEEE Int. Symp. on Parallel and Distributed Processing, p.1-12.
[15]Ferreira, K.B., Riesen, R., Bridges, P., et al., 2014. Accelerating incremental checkpointing for extreme-scale computing. Fut. Gener. Comput. Syst., 30:66-77.
[16]Frasca, M., Prabhakar, R., Raghavan, P., et al., 2011. Virtual I/O caching: dynamic storage cache management for concurrent workloads. Proc. Int. Conf. for High Performance Computing, Networking, Storage and Analysis, p.38.
[17]Gamblin, T., de Supinski, B.R., Schulz, M., et al., 2008. Scalable load-balance measurement for SPMD codes. Proc. ACM/IEEE Conf. on Supercomputing, p.1-12.
[18]Gustafson, J.L., 1988. Reevaluating Amdahl’s law. Commun. ACM, 31(5):532-533.
[19]Hargrove, P.H., Duell, J.C., 2006. Berkeley lab checkpoint/restart (BLCR) for Linux clusters. J. Phys. Conf. Ser., 46(1):494-499.
[20]Hennessy, J.L., Patterson, D.A., 2011. Computer Architecture: a Quantitative Approach. Elsevier.
[21]HPCwire, 2010. DARPA Sets Ubiquitous HPC Program in Motion. Available from http://www.hpcwire.com/2010/08/10/darpa_sets_ubiquitous_hpc_program_in_motion/.
[22]Hu, W., Liu, G.M., Li, Q., et al., 2016. Storage speedup: an effective metric for I/O-intensive parallel application. 18th Int. Conf. on Advanced Communication Technology, p.1-2.
[23]Kalaiselvi, S., Rajaraman, V., 2000. A survey of checkpointing algorithms for parallel and distributed computers. Sadhana, 25(5):489-510.
[24]Kim, Y., Gunasekaran, R., 2015. Understanding I/O workload characteristics of a peta-scale storage system. J. Supercomput., 71(3):761-780.
[25]Kim, Y., Gunasekaran, R., Shipman, G.M., et al., 2010. Workload characterization of a leadership class storage cluster. Petascale Data Storage Workshop, p.1-5.
[26]Kotz, D., Nieuwejaar, N., 1994. Dynamic file-access characteristics of a production parallel scientific workload. Proc. Supercomputing, p.640-649.
[27]Liao, W.K., Ching, A., Coloma, K., et al., 2007. Using MPI file caching to improve parallel write performance for large-scale scientific applications. Proc. ACM/IEEE Conf. on Supercomputing, p.8.
[28]Liu, N., Cope, J., Carns, P., et al., 2012. On the role of burst buffers in leadership-class storage systems. IEEE 28th Symp. on Mass Storage Systems and Technologies, p.1-11.
[29]Liu, Y., Gunasekaran, R., Ma, X.S., et al., 2014. Automatic identification of application I/O signatures from noisy server-side traces. Proc. 12th USENIX Conf. on File and Storage Technologies, p.213-228.
[30]Lu, K., 1999. Research on Parallel File Systems Technology Toward Parallel Computing. PhD Thesis, National University of Defense Technology, Changsha, China (in Chinese).
[31]Lucas, R., Ang, J., Bergman, K., et al., 2014. DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges. USDOE Office of Science.
[32]Miller, E.L., Katz, R.H., 1991. Input/output behavior of supercomputing applications. Proc. ACM/IEEE Conf. on Supercomputing, p.567-576.
[33]Moreira, J., Brutman, M., Castano, J., et al., 2006. Designing a highly-scalable operating system: the blue Gene/L story. Proc. ACM/IEEE Conf. on Supercomputing, p.53-61.
[34]Oldfield, R.A., Arunagiri, S., Teller, P.J., et al., 2007. Modeling the impact of checkpoints on next-generation systems. 24th IEEE Conf. on Mass Storage Systems and Technologies, p.30-46.
[35]Pasquale, B.K., Polyzos, G.C., 1993. A static analysis of I/O characteristics of scientific applications in a production workload. Proc. ACM/IEEE Conf. on Supercomputing, p.388-397.
[36]Plank, J.S., Beck, M., Kingsley, G., et al., 1995. Libckpt: transparent checkpointing under Unix. Proc. USENIX Technical Conf., p.18.
[37]Purakayastha, A., Ellis, C., Kotz, D., et al., 1995. Characterizing parallel file-access patterns on a large-scale multiprocessor. 9th Int. Parallel Processing Symp., p.165-172.
[38]Sisilli, J., 2015. Improved Solutions for I/O Provisioning and Application Acceleration. Available from http://www.flashmemorysummit.com/English/Collaterals/Proceedings/2015/20150811_FD11_Sisilli.pdf [Accessed on Nov. 18, 2015].
[39]Rudin, W., 1976. Principles of Mathematical Analysis. McGraw-Hill Publishing Co.
[40]Shalf, J., Dosanjh, S., Morrison, J., 2011. Exascale computing technology challenges. 9th Int. Conf. on High Performance Computing for Computational Science, p.1-25.
[41]Strohmaier, E., Dongarra, J., Simon, H., et al., 2015. TOP500 Supercomputer Sites. Available from http://www.top500.org/ [Accessed on Dec. 30, 2015].
[42]Sun, X.H., Ni, L.M., 1993. Scalable problems and memory-bounded speedup. J. Parall. Distr. Comput., 19(1): 27-37.
[43]University of California, 2007. IOR HPC Benchmark. Available from http://sourceforge.net/projects/ior-sio/ [Accessed on Sept. 1, 2014].
[44]Wang, F., Xin, Q., Hong, B., et al., 2004. File system workload analysis for large scale scientific computing applications. Proc. 21st IEEE/12th NASA Goddard Conf. on Mass Storage Systems and Technologies, p.139-152.
[45]Wang, T., Oral, S., Wang, Y.D., et al., 2014. Burstmem: a high-performance burst buffer system for scientific applications. IEEE Int. Conf. on Big Data, p.71-79.
[46]Wang, T., Oral, S., Pritchard, M., et al., 2015. Development of a burst buffer system for data-intensive applications. arXiv:{1505.01765}. Available from http://arxiv.org/abs/1505.01765
[47]Wang, Z.Y., 2009. Reliability speedup: an effective metric for parallel application with checkpointing. Int. Conf. on Parallel and Distributed Computing, Applications and Technologies, p.247-254.
[48]Xie, B., Chase, J., Dillow, D., et al., 2012. Characterizing output bottlenecks in a supercomputer. Int. Conf. for High Performance Computing, Networking, Storage and Analysis, p.1-11.
[49]Yang, X.J., Du, J., Wang, Z.Y., 2011. An effective speedup metric for measuring productivity in large-scale parallel computer systems. J. Supercomput., 56(2):164-181.
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