research-article

ACIC: automatic cloud I/O configurator for HPC applications

Authors:

Wenguang ChenAuthors Info & Claims

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Article No.: 38, Pages 1 - 12

https://doi.org/10.1145/2503210.2503216

Published: 17 November 2013 Publication History

Abstract

The cloud has become a promising alternative to traditional HPC centers or in-house clusters. This new environment highlights the I/O bottleneck problem, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing cloud I/O system configurations leads to significant variation in the performance and cost efficiency of I/O intensive HPC applications. However, storage system configuration is tedious and error-prone to do manually, even for experts.

This paper proposes ACIC, which takes a given application running on a given cloud platform, and automatically searches for optimized I/O system configurations. ACIC utilizes machine learning models to perform black-box performance/cost predictions. To tackle the high-dimensional parameter exploration space unique to cloud platforms, we enable affordable, reusable, and incremental training guided by Plackett and Burman Matrices. Results with four representative applications indicate that ACIC consistently identifies near-optimal configurations among a large group of candidate settings.

References

[1]

GPFS: A shared-disk file system for large computing clusters.

[2]

G. Alvarez, E. Borowsky, and S. e. a. Go. Minerva: An Automated Resource Provisioning Tool for Large-scale Storage Systems. ACM Transactions on Computer Systems (TOCS), 19(4):483--518, 2001.

Digital Library

[3]

Amazon Inc. High Performance Computing (HPC). http://aws.amazon.com/ec2/hpc-applications/, 2011.

[4]

E. Anderson, M. Hobbs, K. Keeton, S. Spence, M. Uysal, and A. Veitch. Hippodrome: Running Circles Around Storage Administration. In FAST, 2002.

Digital Library

[5]

M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al. A View of Cloud Computing. Communications of the ACM, 53(4):50--58, 2010.

Digital Library

[6]

S. Browne, J. Dongarra, N. Garner, K. London, and P. Mucci. A Scalable Cross-platform Infrastructure for Application Performance Tuning Using Hardware Counters. In SC. IEEE, 2000.

Digital Library

[7]

S. Byna, Y. Chen, X.-H. Sun, R. Thakur, and W. Gropp. Parallel I/O Prefetching Using MPI File Caching and I/O Signatures. In SC. IEEE, 2008.

Digital Library

[8]

B. Callaghan. NFS Illustrated. Addison-Wesley Longman Ltd., Essex, UK, 2000.

Digital Library

[9]

P. Carns, W. L. III, R. Ross, and R. Thakur. PVFS: A Parallel File System For Linux Clusters. In Proceedings of the 4th Annual Linux Showcase and Conference, 2000.

Digital Library

[10]

Computational Research Division. Madbench2. http://crd-legacy.lbl.gov/~borrill/MADbench2/.

[11]

A. Darling, L. Carey, and W. Feng. The Design, Implementation, and Evaluation of mpiBLAST. In Proceedings of the ClusterWorld Conference and Expo, 2003.

[12]

C. Evangelinos and C. Hill. Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon's EC2. ratio, 2(2.40):2--34, 2008.

[13]

M. Fahey, J. Larkin, and J. Adams. I/O performance on a massively parallel Cray XT3/XT4. In IPDPS. IEEE, 2008.

[14]

M. Folk, A. Cheng, and K. Yates. HDF5: A File Format and I/O Library for High Performance Computing Applications. In SC, volume 99, 1999.

[15]

S. Ghemawat, H. Gobioff, and S. Leung. The Google File System. In SOSP. ACM, 2003.

Digital Library

[16]

A. Gulati, G. Shanmuganathan, I. Ahmad, C. Waldspurger, and M. Uysal. Pesto: Online Storage Performance Management in Virtualized Datacenters. In SOCC, page 19. ACM, 2011.

Digital Library

[17]

H. Herodotou, F. Dong, and S. Babu. No One (cluster) Size Fits All: Automatic Cluster Sizing for Data-intensive Analytics. In SOCC. ACM, 2011.

Digital Library

[18]

Y. Huai, R. Lee, S. Zhang, C. H. Xia, and X. Zhang. DOT: A Matrix Model for Analyzing, Optimizing And Deploying Software for Big Data Analytics in Distributed Systems. In SOCC. ACM, 2011.

Digital Library

[19]

N. Huber, S. Becker, C. Rathfelder, J. Schweflinghaus, and R. H. Reussner. Performance Modeling in Industry: A Case Study on Storage Virtualization. In ICSE. ACM, 2010.

Digital Library

[20]

G. Juve, E. Deelman, K. Vahi, G. Mehta, B. Berriman, B. P. Berman, and P. Maechling. Data Sharing Options for Scientific Workflows on Amazon EC2. In SC, 2010.

Digital Library

[21]

D. J. Kerbyson, H. J. Alme, A. Hoisie, F. Petrini, H. J. Wasserman, and M. Gittings. Predictive Performance And Scalability Modeling of A Large-scale Application. In SC. ACM, 2001.

Digital Library

[22]

A. Konwinski, J. Bent, J. Nunez, and M. Quist. Towards An I/O Tracing Framework Taxonomy. In PDSW. ACM, 2007.

Digital Library

[23]

L. M. Kristensen and L. Petrucci. An Approach to Distributed State Space Exploration for Coloured Petri Nets. In ICATPN. Springer, 2004.

[24]

S. Lang, P. Carns, R. Latham, R. Ross, K. Harms, and W. Allcock. I/O Performance Challenges at Leadership Scale. In SC. ACM, 2009.

Digital Library

[25]

H. Lin, X. Ma, W. Feng, and N. Samatova. Coordinating Computation and I/O in Massively Parallel Sequence Search. IEEE Transactions on Parallel and Distributed Systems, 22(4):529--543, 2011.

Digital Library

[26]

M. Liu, Y. Jin, J. Zhai, Y. Z. Q. Shi, X. Ma, and W. Chen. ACIC Homepage. http://hpc.cs.tsinghua.edu.cn/ACIC, 2013.

[27]

X. Ma, M. Winslett, J. Lee, and S. Yu. Improving MPI-IO Output Performance with Active Buffering Plus Threads. In IPDPS. IEEE, 2003.

Digital Library

[28]

H. Madhyastha, J. McCullough, G. Porter, R. Kapoor, S. Savage, A. Snoeren, and A. Vahdat. scc: Cluster Storage Provisioning Informed by Application Characteristics and SLAs. In FAST. USENIX, 2012.

Digital Library

[29]

G. Marin and J. Mellor-Crummey. Cross-architecture Performance Predictions for Scientific Applications Using Parameterized Models. In SIGMETRICS. ACM, 2004.

Digital Library

[30]

M. Mesnier, M. Wachs, R. Sambasivan, A. Zheng, and G. Ganger. Modeling the Relative Fitness of Storage. In SIGMETRICS. ACM, 2007.

Digital Library

[31]

Message Passing Interface Forum. The Message Passing Interface (MPI) standard. http://www.mpi-forum.org/docs/mpi-2.2/mpi22-report.pdf.

[32]

Mingliang Liu and Jidong Zhai and Yan Zhai and Xiaosong Ma and Wenguang Chen. One Optimized I/O Configuration per HPC Application: Leveraging The Configurability of Cloud. In APSys. ACM, 2011.

Digital Library

[33]

D. Montgomery. Design and analysis of experiments. John Wiley & Sons Inc., 1991.

[34]

National Center for Biotechnology Information. NCBI BLAST. http://www.ncbi.nlm.nih.gov/BLAST/.

[35]

L. Olshen and C. Stone. Classification and Regression Trees. Wadsworth International Group, 1984.

[36]

T. Osogami and S. Kato. Optimizing System Configurations Quickly by Guessing at The Performance. In SIGMETRICS, 2007.

Digital Library

[37]

T. Papaioannou, N. Bonvin, and K. Aberer. Scalia: An Adaptive Scheme for Efficient Multi-Cloud Storage. In SC, 2012.

Digital Library

[38]

R. Plackett and J. Burman. The Design of Optimum Multifactorial Experiments. Biometrika, 33(4):305--325, 1946.

[39]

A. Purakayastha, C. Ellis, D. Kotz, N. Nieuwejaar, and M. Best. Characterizing Parallel File-access Patterns on a Large-scale Multiprocessor. In IPDPS. IEEE, 1995.

Digital Library

[40]

R. Rew and G. Davis. NetCDF: An Interface for Scientific Data Access. Computer Graphics and Applications, IEEE, 10(4):76--82, 1990.

Digital Library

[41]

P. Schwan. Lustre: Building A File System for 1000-node Clusters. In Proceedings of the 2003 Linux Symposium, volume 2003, 2003.

[42]

H. Shan, K. Antypas, and J. Shalf. Characterizing and Predicting the I/O Performance of HPC Applications Using a Parameterized Synthetic Benchmark. In SC. IEEE, 2008.

Digital Library

[43]

K. Shvachko, H. Kuang, S. Radia, and R. Chansler. The Hadoop Distributed File System. In Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium on, pages 1--10. IEEE, 2010.

Digital Library

[44]

J. Sim, A. Dasgupta, H. Kim, and R. Vuduc. A Performance Analysis Framework for Identifying Potential Benefits in GPGPU Applications. In PPoPP. ACM, 2012.

Digital Library

[45]

N. R. Tallent, J. M. Mellor-Crummey, and M. W. Fagan. Binary Analysis for Measurement and Attribution of Program Performance. In PLDI. ACM, 2009.

Digital Library

[46]

V. Taylor, X. Wu, and R. Stevens. Prophesy: An Infrastructure for Performance Analysis And Modeling of Parallel And Grid Applications. In SIGMETRICS. ACM, 2003.

Digital Library

[47]

R. Thakur, W. Gropp, and E. Lusk. Data Sieving and Collective I/O in ROMIO. In FRONTIERS, 1999.

Digital Library

[48]

E. Thereska, B. Doebel, A. Zheng, and P. Nobel. Practical Performance Models for Complex, Popular Applications. In SIGMETRICS. ACM, 2010.

Digital Library

[49]

L. William, M. Tyce, and M. Christopher. IOR HPC Benchmark. https://asc.llnl.gov/sequoia/benchmarks, 2003.

[50]

P. Wong and R. der Wijngaart. NAS Parallel Benchmarks I/O Version 2.4. NASA Ames Research Center Tech. Rep. NAS-03-002, 2003.

[51]

R. Xue, W. Chen, and W. Zheng. CprFS: A User-level File System to Support Consistent File States for Checkpoint and Restart. In ICS. ACM, 2008.

Digital Library

[52]

L. T. Yang, X. Ma, and F. Mueller. Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution. In SC. IEEE, 2005.

Digital Library

[53]

J. Yi, D. Lilja, and D. Hawkins. A Statistically Rigorous Approach for Improving Simulation Methodology. In HPCA. IEEE, 2003.

Digital Library

[54]

J. Zhai, W. Chen, and W. Zheng. Phantom: Predicting Performance of Parallel Applications on Large-scale Parallel Machines Using a Single Node. In PPoPP. ACM, 2010.

Digital Library

[55]

Y. Zhai, M. Liu, J. Zhai, X. Ma, and W. Chen. Cloud Versus In-house Cluster: Evaluating Amazon Cluster Compute Instances for Running MPI Applications. In SC. ACM, 2011.

Digital Library

[56]

M. Zingale. FLASH I/O Benchmark Routine Parallel HDF5. http://www.ucolick.org/~zingale, 2001.

Cited By

Bez JByna SIbrahim S(2023)I/O Access Patterns in HPC Applications: A 360-Degree SurveyACM Computing Surveys10.1145/361100756:2(1-41)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3611007
An ZLi DGuo YGao GRen YJia NHu X(2023)Towards OS Heterogeneity Aware Cluster Management for HPCProceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3609510.3609819(16-23)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3609510.3609819
Hou ZShen HZhou XGu JWang YZhao T(2022)Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directionsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-0625-816:5Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11704-022-0625-8
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ACIC: automatic cloud I/O configurator for HPC applications

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cover image ACM Conferences

SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

November 2013

1123 pages

ISBN:9781450323789

DOI:10.1145/2503210

General Chair:
William Gropp
University of Illinois at Urbana-Champaign, Urbana, Illinois
,
Program Chair:
Satoshi Matsuoka
Tokyo Institute of Technology, Tokyo, Japan

Copyright © 2013 ACM.

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Published: 17 November 2013

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 17 - 21, 2013

Colorado, Denver

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SC '13 Paper Acceptance Rate 91 of 449 submissions, 20%;

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Cited By

Bez JByna SIbrahim S(2023)I/O Access Patterns in HPC Applications: A 360-Degree SurveyACM Computing Surveys10.1145/361100756:2(1-41)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3611007
An ZLi DGuo YGao GRen YJia NHu X(2023)Towards OS Heterogeneity Aware Cluster Management for HPCProceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3609510.3609819(16-23)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3609510.3609819
Hou ZShen HZhou XGu JWang YZhao T(2022)Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directionsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-0625-816:5Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11704-022-0625-8
Ye ZWang YHe SXu CSun X(2021)Sova: A Software-Defined Autonomic Framework for Virtual Network AllocationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.301214632:1(116-130)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TPDS.2020.3012146
Jia YChen F(2020)Kill Two Birds with One Stone: Auto-tuning RocksDB for High Bandwidth and Low Latency2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS47774.2020.00113(652-664)Online publication date: Nov-2020
https://doi.org/10.1109/ICDCS47774.2020.00113
Huang DLiu QKlasky SWang JChoi JLogan JPodhorszki N(2019)Harnessing Data Movement in Virtual Clusters for In-Situ ExecutionIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.286787930:3(615-629)Online publication date: 1-Mar-2019
https://doi.org/10.1109/TPDS.2018.2867879
Huang DLiu QChoi JPodhorszki NKlasky SLogan JOstrouchov GHe XWolf M(2019)Can I/O Variability Be Reduced on QoS-Less HPC Storage Systems?IEEE Transactions on Computers10.1109/TC.2018.288170968:5(631-645)Online publication date: 1-May-2019
https://doi.org/10.1109/TC.2018.2881709
Marathe AHarris RLowenthal Dde Supinski BRountree BSchulz M(2016)Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2IEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.250845727:9(2574-2588)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1109/TPDS.2015.2508457
Costa LAl-Kiswany SRipeanu MYang H(2016)Support for Provisioning and Configuration Decisions for Data Intensive WorkflowsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.249769327:9(2725-2739)Online publication date: 1-Sep-2016
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Nicolae BRiteau PKeahey K(2015)Towards Transparent Throughput Elasticity for IaaS Cloud StorageInternational Journal of Distributed Systems and Technologies10.4018/IJDST.20151001026:4(21-44)Online publication date: 1-Oct-2015
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