Technological forecasting of supercomputer development: The March to Exascale computing

Omega (United Kingdom)

Volumn 51, Issue , 2015, Pages 128-135

  Lim, Dong Joon ^a   Anderson, Timothy R ^a   Shott, Tom ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

Data envelopment analysis; Rate of change; State of the art; Supercomputer; Technological forecasting

Indexed keywords

DATA ENVELOPMENT ANALYSIS; ENERGY EFFICIENCY; HYBRID SYSTEMS; TECHNOLOGICAL FORECASTING; WEATHER FORECASTING;

EXASCALE; EXASCALE COMPUTING; LEADING TECHNOLOGY; MANYCORE SYSTEMS; MASSIVE PARALLELISM; MULTI-CORE SYSTEMS; RATE OF CHANGE; SPEED IMPROVEMENT; STATE OF THE ART; TECHNOLOGICAL PROGRESS;

SUPERCOMPUTERS;

EID: 84920153381     PISSN: 03050483     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.omega.2014.09.009     Document Type: Article

References (59)

1. Lim, DJ, Kocaoglu, DF., China-Can it move from imitation to innovation?. In: Technol. manag. energy smart world (PICMET); 2011 Proc. PICMET'11, n.d. p. 1-13.
2. Feitelson D.G. The supercomputer industry in light of the Top500 data. Comput Sci Eng 2005, 7:42-47.
3. Dongarra, J., Visit to the National University for Defense Technology Changsha, China, Tennessee; 2013.
4. National Research Council, Getting Up to Speed: The Future of Supercomputing, 1st ed., National Academies Press, Washington, D.C; 2005.
5. Dongarra J., Meuer H., Strohmaier E. TOP500 supercomputer sites. Supercomputer 1997, 13:89-111.
6. Peckham M. RIKEN plans exascale supercomputer '30 times faster' than today's fastest in six years. Time 2013.
7. Ashby, S, Beckman, P, Chen, J, Colella, P, Collins, B, Crawford, D., et al., The opportunities and challenges of Exascale computing, U.S Dep. Energy Office Sci.; 2010. p. 1-71.
8. Simon, H, Zacharia, T, Stevens, R., Modeling and simulation at the exascale for energy and the environment, Dep. Energy Tech. Rep.; 2007.
9. Hsu, C-H, Kuehn, JA, Poole, SW., Towards efficient supercomputing. In: Proc. Third Jt. WOSP/SIPEW int. conf. perform. eng.-ICPE'12, ACM Press, New York, New York, USA; 2012: p. 157.
10. Kamil, S, Shalf, J, Strohmaier, E., Power efficiency in high performance computing. In: 2008 IEEE int. symp. parallel distrib. process., IEEE; 2008. p. 1-8.
11. Robert F. Who will step up to exascale?. Science 2013, 80:264-266.
12. Geller T. Supercomputing's exaflop target. Commun ACM 2011, 54:16.
13. Simon, H., Why we need exascale and why we won't get there by 2020. In: Opt. interconnects conf., Lawrence Berkeley National Laboratory, Santa Fe; 2013.
14. Owens J.D., Houston M., Luebke D., Green S., Stone J.E., Phillips J.C. GPU computing. Proc IEEE 2008, 96:879-899.
15. Li Sheng, Ahn, J-H, Strong, RD, Brockman, JB, Tullsen, DM, Jouppi, NP., McPAT: An integrated power, area, and timing modeling framework for multicore and manycore architectures. In: MICRO-42. 42nd Annu. IEEE/ACM int. symp. on. IEEE, IEEE, New York; 2009. p. 469-480.
16. Panziera J.P. Five challenges for future supercomputers. Supercomputers 2012, 38-40.
17. Vuduc R., Czechowski K. What GPU computing means for high-end systems. IEEE Micro 2011, 74-78.
18. Rajovic, N, Carpenter, P, Gelado, I, Puzovic, N, Ramirez, A., Are mobile processors ready for HPC?. In: Supercomput. conf., barcelona supercomputing center, Denver; 2013.
19. Bogetoft P., Otto L. Benchmarking with DEA, SFA, and R 2010, Springer, New York. 1st ed.
20. Giokas D. Bank branch operating efficiency: a comparative application of DEA and the loglinear model. Omega 1991, 19:549-557.
21. Liu J.S., Lu L.Y.Y., Lu W.-M., Lin B.J.Y. A survey of DEA applications. Omega 2013, 41:893-902.
22. Thanassoulis E., Boussofiane A., Dyson R.G. A comparison of data envelopment analysis and ratio analysis as tools for performance assessment. Omega 1996, 24:229-244.
23. Anderson, TR, Hollingsworth, K, Inman, L., Assessing the rate of change in the enterprise database system market over time using DEA. In: PICMET'01. Portl. int. conf. manag. eng. technol.; 2001. p. 203.
24. Cole B.F. An evolutionary method for synthesizing technological planning and architectural advance 2009, Georgia Institute of Technology.
25. Tudorie A.A. Technology forecasting of electric vehicles using data envelopment analysis. Delft Univ Technol 2012.
26. Durmusoglu, A, Dereli, T., On the technology forecasting using data envelopment analysis (TFDEA). In: Technol. manag. energy smart world (PICMET), 2011 proc. PICMET '11, PICMET, U.S. Portland, 2011.
27. Kim J.-O., Kim J.-H., Kim S.-K. A hybrid technological forecasting model by identifying the efficient DMUs: an application to the main battle tank. Asian J Technol Innov 2007, 15:83-102.
28. Lim, D-J, Anderson, TR, Kim, J. Forecast of wireless communication technology: a comparative study of regression and TFDEA Model. In: Technol. manag. emerg. technol., PICMET, Vancouver, Canada; 2012. p. 1247-1253.
29. Fried H.O., Lovell C.A.K., Schmidt S.S. The measurement of productive efficiency and productivity growth 2008, Oxford University Press, New York. 1st ed.
30. Nakabayashi K., Tone K. Egoist's dilemma: a DEA game. Omega 2006, 34:135-148.
31. Doyle J.R., Green R.H. Comparing products using data envelopment analysis. Omega 1991, 19:631-638.
32. Cook W.D., Tone K., Zhu J. Data envelopment analysis: prior to choosing a model. Omega 2014, 44:1-4.
33. Alexander A.J., Nelson J.R. Measuring technological change: aircraft turbine engines. Technol Forecast Soc Change 1973, 5:189-203.
34. Martino J.P. Measurement of technology using tradeoff surfaces. Technol Forecast Soc Change 1985, 27:147-160.
35. Lim D.-J., Anderson T.R., Inman O.L. Choosing effective dates from multiple optima in technology forecasting using data envelopment analysis (TFDEA). Technol Forecast Soc Change 2014, 88:91-97.
36. Lim D-J, Anderson TR, Improving Forecast Accuracy by a Segmented Rate of Change in Technology Forecasting using Data Envelopment Analysis, in: Infrastruct. Serv. Integr., PICMET, Kanazawa, 2014: pp. 2903-2907.
37. Lim D.-J., Jahromi S.R., Anderson T.R., Tudorie A.A. Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments. Technol Forecast Soc Change 2014, http://dx.doi.org/10.1016/j.techfore.2014.05.008.
38. Dongarra J.J., Luszczek P., Petitet A. The LINPACK Benchmark: past, present and future. Concurr Comput Pract Exp 2003, 15:803-820.
39. Lim, D-J, Anderson, TR., An introduction to technology forecasting with a TFDEA excel add-in. In: Technol. manag. emerg. technol. (PICMET), 2012 proc. PICMET '12; 2012.
40. Lim D.-J., Runde N., Anderson T.R. Applying technology forecasting to new product development target setting of LCD panels. Adv bus manag forecast 2013, 340. Emerald Group Publishing Limited. 9th ed. R. Klimberg, K.D. Lawrence (Eds.).
41. Anderson, TR, Inman, L., Resolving the issue of multiple optima in technology forecasting using data envelopment analysis. In: Technol. manag. energy smart world, PICMET; 2011. p. 1-5.
42. Wood P. Cray Inc. replacing IBM to build UI supercomputer. News Gaz 2011, 1.
43. Lawrence L. Cray jumped from AMD for the flexibility and performance of Intel chips. Inquiry 2012.
44. Murariu C. Intel signs deal with cray, marks AMD's departure from cray's supercomputers. Softpedia 2012.
45. Wang F., Yang C.-Q., Du Y.-F., Chen J., Yi H.-Z., Xu W.-X. Optimizing linpack benchmark on GPU-accelerated petascale supercomputer. J Comput Sci Technol 2011, 26:854-865.
46. Cole A. Intel and InfiniBand: pure HPC play, or is there a fabric in the works?. IT Bus Edge 2012.
47. InfiniBand strengthens leadership as the high-speed interconnect of choice, Mellanox Technol.; 2012. p. 1-31.
48. Whittaker Z. Intel buys QLogic's InfiniBand assets for $125 million. ZD Net 2012.
49. IntelPR, intel acquires industry-leading, high-performance computing interconnect technology and expertise, Intel Newsroom; 2012.
50. Morgan T. Intel came a-knockin' for Cray super interconnects. Register 2012.
51. Haring R, Ohmacht M, Fox T, Gschwind M, Boyle P, Chist N, et al. The IBM Blue Gene/Q Compute Chip, Ieee Micro 32; 2011, 48-60.
52. Lazou C, Should I. Buy GPGPUs or Blue Gene?, HPCwire; 2010.
53. Feldman M. Blue Genes and GPU Clusters Top the Latest Green500, HPCwire; 2011.
54. Tashman L.J. Out-of-sample tests of forecasting accuracy: an analysis and review. Int J Forecast 2000, 16:437-450.
55. Kimihiko H. Exascale supercomputer project. Kobe 2013.
56. Tung L. After X86 servers, is IBM gearing up to sell its chips business too?, ZDNet; 2014.
57. Nick Davis Cray CS300 cluster supercomputers to feature new intel(R) Xeon Phi(TM) coprocessors x100 family. Cray Media 2013.
58. Nick Davis Cray to acquire appro international. Cray Media 2012.
59. Aroca R.V., Gonçalves L.M.G. Towards green data centers: a comparison of x86 and ARM architectures power efficiency. J Parallel Distrib Comput 2012, 72:1770-1780.