Abstract
In pursue of exaflop computing, the expenses of HPC centers increase in terms of acquisition, energy, employment, and programming. Thus, a quantifiable metric for productivity as value per cost gets more important to make an informed decision on how to invest available budgets. In this work, we model overall productivity from a computing center’s perspective. The productivity model uses as value the number of application runs possible during the lifetime of a given supercomputer. The cost is the total cost of ownership (TCO) of an HPC center including costs for administration and programming effort. For the latter, we include techniques for software cost estimation of large codes taken from the domain of software engineering. As tuning effort increases when more performance is required, we further focus on the impact of the 80-20 rule when it comes to development effort. Here, performance can be expressed with respect to Amdahl’s law. Moreover, we include an asymptotic analysis for parameters like number of compute nodes and lifetime. We evaluate our approach on a real-world case: an engineering application in our integrative hosting environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Apon, A., Ahalt, S., Dantuluri, V., Gurdgiev, C., Limayem, M., Ngo, L., Stealey, M.: High performance computing instrumentation and research productivity in US universities. J. Inf. Technol. Impact 10(2), 87–98 (2010)
Boehm, B., Abts, C., Brown, A.W., Chulani, S., Clark, B., Horowitz, E., Madachy, R., Reifer, D., Steece, B.: COCOMO II Model Definition Manual, Version 2.1. Technical report, University of Southern California (2000)
Chew, W.: No-nonsense guide to measuring productivity. Harvard Bus. Rev. 66(1), 110–118 (1988)
Culler, D.E., Karp, R.M., Patterson, D.A., Sahay, A., Schauser, K.E., Santos, E., Subramonian, R., von Eicken, T.: LogP: Towards a Realistic Model of Parallel Computation. Technical report, Berkeley (1992)
Dongarra, J., Graybill, R., Harrod, W., Lucas, R., Lusk, E., Luszczek, P., Mcmahon, J., Snavely, A., Vetter, J., Yelick, K., Alam, S., Campbell, R., Carrington, L., Chen, T.Y., Khalili, O., Meredith, J., Tikir, M.: DARPA’s HPCS program: history, models, tools, languages. In: Zelkowitz, M.V. (ed.) Advances in COMPUTERS High Performance Computing, Advances in Computers, vol. 72, pp. 1–100. Elsevier (2008)
Ebcioglu, K., Sarkar, V., El-Ghazawi, T., Urbanic, J., Center, P.: An experiment in measuring the productivity of three parallel programming languages. In: Workshop on Productivity and Performance in High-End Computing (P-PHEC), pp. 30–36 (2006)
European Commission: Guide to Financial Issues relating to FP7 Indirect Actions (2013)
Faulk, S., Gustafson, J., Johnson, P., Porter, A., Tichy, W., Votta, L.: Measuring high performance computing productivity. Int. J. High Perform. Comput. Appl. 18(4), 459–473 (2004)
German Science Foundation (DFG): Personalmittelsätze der DFG für das Jahr (2013)
Göbbert, J.H., Gauding, M.: psOpen (2015). http://www.fz-juelich.de/ias/jsc/EN/Expertise/High-Q-Club/psOpen/_node.html
InfiniBand Trade Association (2015). http://www.infinibandta.org/
Intel Corporation: Intel Processor ARK (2015). http://ark.intel.com/
Joseph, E.C., Conway, S., Dekate, C.: Creating Economic Models Showing the Relationship Between Investments in HPC and the Resulting Financial ROI and Innovation and How It Can Impact a Nation’s Competitiveness and Innovation. International Data Corporation (IDC), Technical report (2013)
Kennedy, K., Koelbel, C., Schreiber, R.: Defining and measuring the productivity of programming languages. Int. J. High Perform. Comput. Appl. 18(4), 441–448 (2004)
Kepner, J.: High performance computing productivity model synthesis. Int. J. High Perform. Comput. Appl. 18(4), 505–516 (2004)
McConnell, S.: Software Estimation: Demystifying the Black Art. Redmond, Wa. Microsoft Press (2006)
McCracken, M., Wolter, N., Snavely, A.: Beyond performance tools: Measuring and modeling productivity in HPC. In: Third International Workshop on Software Engineering for High Performance Computing Applications, SE-HPC 2007, pp. 4–4 (2007)
Murphy, D., Nash, T., Lawrence Votta, J., Kepner, J.: A System-wide Productivity Figure of Merit. CT Watch Quarterly 2(4B) (2006)
Newman, M.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)
Pekurovsky, D.: P3DFFT: a framework for parallel computations of Fourier transforms in three dimensions. SIAM J. Sci. Comput. 34(4), C192–C209 (2012)
Sadowski, C., Shewmaker, A.: The Last Mile: Parallel Programming and Usability. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, FoSER 2010, pp. 309–314. ACM, New York (2010)
Snir, M., Bader, D.A.: A framework for measuring supercomputer productivity. Int. J. High Perform. Comput. Appl. 18(4), 417–432 (2004)
Sterling, T.: Productivity metrics and models for high performance computing. Int. J. High Perform. Comput. Appl. 18(4), 433–440 (2004)
Wang, L., Khan, S.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomputing 63(3), 639–656 (2011)
Wienke, S., Iliev, H., Hahnfeld, J., an Mey, D., Müller, M.S.: \(aixH(PC)^2\) - Aachen HPC Productivity Calculator (2015). http://www.hpc.rwth-aachen.de/research/tco/
Wienke, S., an Mey, D., Müller, M.S.: Accelerators for technical computing: is it worth the pain? A TCO perspective. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2013. LNCS, vol. 7905, pp. 330–342. Springer, Heidelberg (2013)
Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65–76 (2009)
Zelkowitz, M., Basili, V., Asgari, S., Hochstein, L., Hollingsworth, J., Nakamura, T.: Measuring productivity on high performance computers. In: IEEE International Symposium on Software Metrics, p. 6 (2005). http://doi.ieeecomputersociety.org/10.1109/METRICS.2005.33
Zelkowitz, M., Hollingsworth, J., Basili, V., Asgari, S., Shull, F., Carver, J., Hochstein, L.: Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers. SC Conference 35 (2005). http://dx.doi.org/10.1109/SC.2005.53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wienke, S., Iliev, H., an Mey, D., Müller, M.S. (2015). Modeling the Productivity of HPC Systems on a Computing Center Scale. In: Kunkel, J., Ludwig, T. (eds) High Performance Computing. ISC High Performance 2015. Lecture Notes in Computer Science(), vol 9137. Springer, Cham. https://doi.org/10.1007/978-3-319-20119-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-20119-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20118-4
Online ISBN: 978-3-319-20119-1
eBook Packages: Computer ScienceComputer Science (R0)