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An Introduction to Big Data, High Performance Computing, High-Throughput Computing, and Hadoop Read chapter Read first chapter

2016 | OriginalPaper | Chapter

3. Data Movement in Data-Intensive High Performance Computing

Authors : Pietro Cicotti, Sarp Oral, Gokcen Kestor, Roberto Gioiosa, Shawn Strande, Michela Taufer, James H. Rogers, Hasan Abbasi, Jason Hill, Laura Carrington

Published in: Conquering Big Data with High Performance Computing

Publisher: Springer International Publishing

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Abstract

The cost of executing a floating point operation has been decreasing for decades at a much higher rate than that of moving data. Bandwidth and latency, two key metrics that determine the cost of moving data, have degraded significantly relative to processor cycle time and execution rate. Despite the limitation of sub-micron processor technology and the end of Dennard scaling, this trend will continue in the short-term making data movement a performance-limiting factor and an energy/power efficiency concern. Even more so in the context of large-scale and data-intensive systems and workloads. This chapter gives an overview of the aspects of moving data across a system, from the storage system to the computing system down to the node and processor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.

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previous chapter Using High Performance Computing for Conquering Big Data
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Footnotes
1
A director-class switch is loosely defined as a high port count switch connecting different fabrics.
 
Literature
1.
2.
3.
4.
S. Amarasinghe, D. Campbell, W. Carlson, A. Chien, W. Dally, E. Elnohazy, M. Hall, R. Harrison, W. Harrod, K. Hill, A. Snavely, ExaScale software study: software challenges in Extreme scale systems. Technical report, DARPA IPTO, Air Force Research Labs, 2009
5.
S. Amarasinghe, M. Hall, R. Lethin, K. Pingali, D. Quinlan, V. Sarkar, J. Shalf, R. Lucas, K. Yelick, P. Balaji, P. C. Diniz, A. Koniges, M. Snir, S.R. Sachs, Report of the Workshop on Exascale Programming Challenges. Technical report, US Department of Energy, 2011
6.
J.A. Ang, B.W. Barrett, K.B. Wheeler, R.C. Murphy, Introducing the graph 500, in Proceedings of Cray User’s Group Meeting (CUG), May 2010
7.
T. Baer, V. Hazlewood, J. Heo, R. Mohr, J. Walsh, Large Lustre File System Experiences at NICS, Cray User Group, Atlanta, GA, May 2009
8.
J.D. Bakos, High-performance heterogeneous computing with the convey hc-1. Comput. Sci. Eng. 12 (6), 80–87 (2010)CrossRef
9.
T. Barrett, M. Sumit, K. Taek-Jun, S. Ravinder, C. Sachit, J. Sondeen, J. Draper, A double-data rate (DDR) processing-in-memory (PIM) device with wideword floating-point capability, in Proceedings of the IEEE International Symposium on Circuits and Systems, 2006
10.
K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, W. Harrod, J. Hiller, S. Karp, S. Keckler, D. Klein, R. Lucas, M. Richards, A. Scarpelli, S. Scott, A. Snavely, T. Sterling, R.S. Williams, K. Yelick, K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, W. Harrod, J. Hiller, S. Keckler, D. Klein, P. Kogge, R.S. Williams, K. Yelick, Exascale computing study: technology challenges in achieving exascale systems. Peter Kogge, editor and study lead (2008)
11.
K. Beyls, E.H. D’Hollander, Reuse distance as a metric for cache behavior, in Proceedings of the IASTED Conference on Parallel and Distributed Computing System, 2001
12.
K. Beyls, E.H. D’Hollander, Reuse distance-based cache hint selection, in Proceedings of the 8th International Euro-Par Conference on Parallel Processing, Euro-Par ’02, London, 2002 (Springer-Verlag, UK, 2002, pp. 265–274)
13.
A.S. Bland, R.A. Kendall, D.B. Kothe, J.H. Rogers, G.M. Shipman, Jaguar: the world’s most powerful computer. Memory (TB) 300 (62), 362 (2009)
14.
A.S. Bland, J.C. Wells, O.E. Messer, O.R. Hernandez, J.H. Rogers, Titan: early experience with the Cray XK6 at oak ridge national laboratory, in Proceedings of Cray User Group Conference (CUG 2012), 2012
15.
Z. Budimlić, M. Burke, V. Cavé, K. Knobe, G. Lowney, R. Newton, J. Palsberg, D. Peixotto, V. Sarkar, F. Schlimbach, S. Tacsirlar, Concurrent collections. Sci. Program. 18 (3–4), 203–217 (2010)
16.
J.A. Butts, G.S. Sohi, A static power model for architects, in Proceedings of the 33rd Annual ACM/IEEE International Symposium on Microarchitecture, MICRO 33 (ACM, New York, NY, 2000), pp. 191–201
17.
P. Cicotti, Tarragon: a programming model for latency-hiding scientific computations. Ph.D. thesis, La Jolla, CA, USA, 2011. AAI3449479
18.
P. Cicotti, L. Carrington, ADAMANT: tools to capture, analyze, and manage data movement, in International Conference on Computational Science (ICCS) San Diego, California, USA, 2016
19.
P. Cicotti, M. Norman, R. Sinkovits, A. Snavely, S. Strande, Gordon: A Novel Architecture for Data Intensive Computing volume On the road to Exascale Computing: Contemporary Architectures in High Performance Computing, chapter 17 Chapman and Hall (2013)
20.
B. Dally, Power, programmability, and granularity: the challenges of exascale computing, in Proceedings of International Parallel and Distributed Processing Symposium (2011), pp. 878–878
21.
J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51 (1), 107–113 (2008)CrossRef
22.
J. Demmel, Communication avoiding algorithms, in Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC ’12 (IEEE Computer Society, Washington, DC, 2012), pp. 1942–2000
23.
J. Deslippe, B. Austin, C. Daley, W.-S. Yang, Lessons learned from optimizing science kernels for intel’s knights landing. Comput. Sci. Eng. 17 (3), 30–42 (2015)CrossRef
24.
D. Dillow, G.M. Shipman, S. Oral, Z. Zhang, Y. Kim et al., Enhancing i/o throughput via efficient routing and placement for large-scale parallel file systems, in Performance Computing and Communications Conference (IPCCC), 2011 IEEE 30th International (IEEE, 2011), pp. 1–9
25.
C. Ding, Y. Zhong, Predicting whole-program locality through reuse distance analysis, in Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation, PLDI ’03 (ACM, New York, NY, 2003), pp. 245–257
26.
H. Dong, H. Nak, D. Lewis, H.-H. Lee, An optimized 3d-stacked memory architecture by exploiting excessive, high-density TSV bandwidth, in IEEE 16th International Symposium on High Performance Computer Architecture (HPCA) (2010), pp. 1–12
27.
J. Draper, J. Chame, M. Hall, C. Steele, T. Barrett, J. LaCoss, J. Granacki, J. Shin, C. Chen, C. W. Kang, I. Kim, G. Daglikoca, The architecture of the diva processing-in-memory chip, in Proceedings of the 16th International Conference on Supercomputing (2002), pp. 14–25
28.
T. Estrada, B. Zhang, P. Cicotti, R. Armen, M. Taufer, A scalable and accurate method for classifying protein–ligand binding geometries using a MapReduce approach. Comput. Biol. Med. 42 (7), 758–771 (2012)CrossRef
29.
M. Ezell, D. Dillow, S. Oral, F. Wang, D. Tiwari, D.E. Maxwell, D. Leverman, J. Hill, I/O router placement and fine-grained routing on titan to support spider II, in Proceedings of Cray User Group Conference (CUG 2014) (2014)
30.
W.-C. Feng, K.W. Cameron, The green500 list: encouraging sustainable supercomputing. Computer 40 (12), 50–55 (2007)CrossRef
31.
B. Fitzpatrick, Distributed caching with memcached. Linux J. 2004 (124), 5 (2004)
32.
R. Ge, X. Feng, S. Song, H.-C. Chang, D. Li, K.W. Cameron, PowerPack: energy profiling and analysis of high-performance systems and applications. IEEE Trans. Parallel Distrib. Syst. 21 (5), 658–671 (2010)CrossRef
33.
J.J. Hack, M.E. Papka, Big data: next-generation machines for big science. Comput. Sci. Eng. 17 (4), 63–65 (2015)CrossRef
34.
Y. Ho, G. Huang, P. Li, Nonvolatile memristor memory: device characteristics and design implications, in IEEE/ACM International Conference on Computer-Aided Design (2009), pp. 485–490
35.
Intel Corporation. Intel®; 64 and IA-32 Architectures Software Developer’s Manual (2015)
36.
G. Kestor, R. Gioiosa, D. Kerbyson, A. Hoisie, Quantifying the energy cost of data movement in scientific applications, in The 16th IEEE International Symposium on Workload Characterization (IISWC), September 2013
37.
P. Kogge, K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, A. Snavely, W. Harrod, K. Hill, Exascale computing study: technology challenges in achieving exascale systems. Technical report, DARPA IPTO, Air Force Research Labs, 2008
38.
A. Krishnamurthy, D.E. Culler, A. Dusseau, S.C. Goldstein, S. Lumetta, T. von Eicken, K. Yelick, Parallel programming in Split-C, in Proceedings of the 1993 ACM/IEEE Conference on Supercomputing, Supercomputing ’93 (ACM, New York, NY, 1993), pp. 262–273
39.
M. Laurenzano, M. Tikir, L. Carrington, A. Snavely, PEBIL: efficient static binary instrumentation for Linux, in 2010 IEEE International Symposium on Performance Analysis of Systems Software (ISPASS), March 2010, pp. 175–183
40.
J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, Z. Ghahramani, Kronecker graphs: an approach to modeling networks. J. Mach. Learn. Res. 11, 985–1042 (2010)MathSciNetMATH
41.
N. Liu, J. Cope, P. Carns, C. Carothers, R. Ross, G. Grider, A. Crume, C. Maltzahn, On the role of burst buffers in leadership-class storage systems, in 2012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) (IEEE, 2012), pp. 1–11
42.
G. Loh, 3D-Stacked memory architectures for multi-core processors, in Proceedings of the International Symposium on Computer Architecture (2008), pp. 453–464
43.
C.-K. Luk, R. Cohn, R. Muth, H. Patil, A. Klauser, G. Lowney, S. Wallace, V.J. Reddi, K. Hazelwood, Pin: building customized program analysis tools with dynamic instrumentation. SIGPLAN Not. 40 (6), 190–200 (2005)CrossRef
44.
H. Meuer, E. Strohmaier, J. Dongarra, H. Simon, Top500 supercomputing sites, 2011
45.
S. Oral, J. Simmons, J. Hill, D. Leverman, F. Wang, M. Ezell, R. Miller, D. Fuller, R. Gunasekaran, Y. Kim et al., Best practices and lessons learned from deploying and operating large-scale data-centric parallel file systems, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (IEEE Press, 2014), pp. 217–228
46.
D.A. Patterson, Latency lags bandwidth. Commun. ACM 47 (10), 71–75 (2004)CrossRef
47.
S.J. Plimpton, K.D. Devine, MapReduce in MPI for large-scale graph algorithms. Parallel Comput. 37 (9), 610–632 (2011). Emerging Programming Paradigms for Large-Scale Scientific Computing
48.
G. Shipman, D. Dillow, S. Oral, F. Wang, The spider center wide file system: from concept to reality, in Proceedings, Cray User Group (CUG) Conference, Atlanta, GA, 2009
49.
M. Taufer, M. Crowley, D. Price, A. Chien, C. Brooks III, Study of a highly accurate and fast protein-ligand docking based on molecular dynamics, in Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International, April 2004, p. 188
50.
M. Taufer, R. Armen, J. Chen, P. Teller, C. Brooks, Computational multiscale modeling in protein–ligand docking. Eng. Med. Biol. Mag. IEEE 28 (2), 58–69 (2009)CrossRef
51.
J. Towns, T. Cockerill, M. Dahan, I. Foster, K. Gaither, A. Grimshaw, V. Hazlewood, S. Lathrop, D. Lifka, G. D. Peterson, R. Roskies, J.R. Scott, N. Wilkens-Diehr, Xsede: accelerating scientific discovery. Comput. Sci. Eng. 16 (5), 62–74 (2014)CrossRef
52.
F. Wang, S. Oral, G. Shipman, O. Drokin, T. Wang, I. Huang, Understanding Lustre filesystem internals. Oak Ridge National Laboratory, National Center for Computational Sciences, Tech. Rep, 2009CrossRef
53.
W.A. Wulf, S.A. McKee, Hitting the memory wall: implications of the obvious. SIGARCH Comput. Archit. News 23 (1), 20–24 (1995)CrossRef
54.
W. Xu, Y. Lu, Q. Li, E. Zhou, Z. Song, Y. Dong, W. Zhang, D. Wei, X. Zhang, H. Chen et al., Hybrid hierarchy storage system in milkyway-2 supercomputer. Front. Comput. Sci. 8 (3), 367–377 (2014)MathSciNetCrossRef
55.
B. Zhang, T. Estrada, P. Cicotti, M. Taufer, On efficiently capturing scientific properties in distributed big data without moving the data: a case study in distributed structural biology using mapreduce, in Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering, CSE ’13 (IEEE Computer Society, Washington, DC, 2013), pp. 117–124
56.
B. Zhang, T. Estrada, P. Cicotti, M. Taufer, Enabling in-situ data analysis for large protein-folding trajectory datasets, in Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS ’14 (IEEE Computer Society, Washington, DC, 2014), pp. 221–230
57.
B. Zhang, T. Estrada, P. Cicotti, P. Balaji, M. Taufer, Accurate scoring of drug conformations at the extreme scale, in Proceedings of 8th IEEE International Scalable Computing Challenge - Co-located with IEEE/ACM CCGrid, 2015
Metadata
Title
Data Movement in Data-Intensive High Performance Computing
Authors
Pietro Cicotti
Sarp Oral
Gokcen Kestor
Roberto Gioiosa
Shawn Strande
Michela Taufer
James H. Rogers
Hasan Abbasi
Jason Hill
Laura Carrington
Copyright Year
2016
Book
Conquering Big Data with High Performance Computing

Print ISBN: 978-3-319-33740-1

Electronic ISBN: 978-3-319-33742-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-33742-5_3

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