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Energy efficient communications in quantum chemistry applications

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Computer Science - Research and Development

Abstract

Modern supercomputing platform designers are becoming increasingly aware of the operational costs and reliability issues, which are rising due to high power consumption of such systems. At the same time, high-performance application developers are taking pro-active steps towards less energy consumption without a significant performance loss. One way to accomplish energy savings during application execution is to change the processor frequency dynamically when processor is not busy, such as during certain communication stages. Previously, the authors have proposed a runtime procedure that identifies communication phases in parallel applications to apply frequency scaling efficiently and without much overhead. The present work applies the phase detection procedure to parallel electronic structure calculations, performed by a widely used package GAMESS. High computational intensity of these calculations and the GAMESS communication model, which distinguishes computation and communication processes, motivated the investigations in this paper. They have led to several insights as to the role of process-core mapping in the application of dynamic frequency scaling during communications.

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Notes

  1. http://www.top500.org/.

  2. MPI Forum: http://www.mpi-forum.org.

  3. Infiniband: http://www.infinibandta.org.

  4. Funded and operated jointly by Iowa State University and Ames Laboratory.

  5. https://www.wattsupmeters.com.

  6. http://mvapich.cse.ohio-state.edu/.

References

  1. Bailey DH, Barszcz E, Barton JT, Browning DS, Carter RL, Dagum L, Fatoohi RA, Frederickson PO, Lasinski TA, Schreiber RS, Simon HD, Venkatakrishnan V, Weeratunga SK (1991) The NAS parallel benchmarks—summary and preliminary results. In: Proceedings of the 1991 ACM/IEEE conference on supercomputing, pp 158–165

    Chapter  Google Scholar 

  2. Bertran R, Gonzalez M, Martorell X, Navarro N, Ayguade E (2010) Decomposable and responsive power models for multicore processors using performance counters. In: Proceedings of the 24th ACM international conference on supercomputing (ICS ’10). ACM, New York, pp 147–158, ACM

    Chapter  Google Scholar 

  3. Chaparro P, Gonzalez J, Magklis G, Cai Q, Gonzalez A (2007) Understanding the thermal implications of multi-core architectures. IEEE Trans Parallel Distrib Syst 18(8):1055–1065

    Article  Google Scholar 

  4. Curtis-Maury M, Shah A, Blagojevic F, Nikolopoulos DS, de Supinski BR, Schulz M (2008) Prediction models for multi-dimensional power-performance optimization on many cores. In: Proceedings of the 17th international conference on parallel architectures and compilation techniques (PACT ’08). ACM, New York, pp 250–259

    Chapter  Google Scholar 

  5. David H, Gorbatov E, Hanebutte UR, Khannal R, Le C (2010) Rapl: memory power estimation and capping. In: Proceedings of the 16th ACM/IEEE international symposium on low power electronics and design (ISLPED ’10). ACM, New York, pp 189–194

    Chapter  Google Scholar 

  6. Demmel J, Hoemmen M, Mohiyuddin M, Yelick KA (2008) Avoiding communication in sparse matrix computations. In: 22nd IEEE international symposium on parallel and distributed processing (IPDPS 2008), Miami, FL, USA, April 14–18, 2008 pp 1–12

    Chapter  Google Scholar 

  7. Donald J, Martonosi M (2006) Techniques for multicore thermal management: classification and new exploration. In: Proceedings of the 33rd annual international symposium on computer architecture (ISCA ’06), Washington, DC, USA. IEEE Comput Soc, Los Alamitos, pp 78–88

    Chapter  Google Scholar 

  8. Fedorov DG, Olson RM, Kitaura K, Gordon MS, Koseki S (2004) A new hierarchical parallelization scheme: generalized distributed data interface (GDDI), and an application to the fragment molecular orbital method (FMO). J Comput Chem 25(6):872–880

    Article  Google Scholar 

  9. Fletcher GD, Schmidt MW, Bode BM, Gordon MS (2000) The distributed data interface in GAMESS. Comput Phys Commun 128(1–2):190–200

    Article  MATH  Google Scholar 

  10. Freeh VW, Lowenthal DK (2005) Using multiple energy gears in MPI programs on a power-scalable cluster. In: Proceedings of the tenth ACM SIGPLAN symposium on principles and practice of parallel programming, pp 164–173

    Google Scholar 

  11. Ge R, Feng X, Feng W, Cameron KW (2007) CPU MISER: A performance-directed, run-time system for power-aware clusters. In: International conference on parallel processing, 2007 (ICPP 2007), p 18

    Chapter  Google Scholar 

  12. Ge R, Feng X, Song S, Chang HC, Li D, Cameron KW (2010) PowerPack: energy profiling and analysis of high-performance systems and applications. IEEE Trans Parallel Distrib Syst 21:658–671

    Article  Google Scholar 

  13. Gordon MS, Schmidt MW (2005) In: Dykstra CE, Frenking G, Kim KS, Scuseria GE (eds) Advances in electronic structure theory: games a decade later. Theory and applications of computational chemistry: the first forty years

    Google Scholar 

  14. Hanson H, Keckler SW, Ghiasi S, Rajamani K, Rawson F, Rubio J (2007) Thermal response to dvfs: analysis with an intel pentium m. In: Proceedings of the 2007 international symposium on low power electronics and design (ISLPED ’07). ACM, New York, pp 219–224

    Chapter  Google Scholar 

  15. Hsu CH, Feng W (2005) A power-aware run-time system for high-performance computing. In: Supercomputing, 2005: proceedings of the ACM/IEEE SC conference, Nov 2005, p 1

    Google Scholar 

  16. Huang S, Feng W (2009) Energy-efficient cluster computing via accurate workload characterization. In: 9th IEEE/ACM international symposium on cluster computing and the grid, 2009 (CCGRID ’09), May 2009, pp 68–75

    Chapter  Google Scholar 

  17. Ioannou N, Kauschke M, Gries M, Cintra M (2011) Phase-based application-driven hierarchical power management on the single-chip cloud computer. In: 2011 international conference on parallel architectures and compilation techniques (PACT), Oct 2011, pp 131–142

    Chapter  Google Scholar 

  18. Kandalla K, Mancini EP, Sur S, Panda DK (2010) Designing power-aware collective communication algorithms for InfiniBand clusters. In: 2010 39th international conference on parallel processing (ICPP), pp 218–227

    Chapter  Google Scholar 

  19. Lim MY, Freeh VW, Lowenthal DK (2011) Adaptive, transparent CPU scaling algorithms leveraging inter-node MPI communication regions. Parallel Comput 37(10–11):667–683

    Article  Google Scholar 

  20. Liu J, Poff D, Abali B (2009) Evaluating high performance communication: a power perspective. In: Proceedings of the 23rd international conference on supercomputing, pp 326–337

    Chapter  Google Scholar 

  21. Olson RM, Schmidt MW, Gordon MS, Rendell AP (2003) Enabling the efficient use of SMP clusters: the GAMESS/DDI model. In: SC, p 41

    Google Scholar 

  22. Park J, Shin D, Chang N, Pedram M (2010) Accurate modeling and calculation of delay and energy overheads of dynamic voltage scaling in modern high-performance microprocessors. In: 2010 international symposium on low-power electronics and design (ISLPED), pp 419–424

    Google Scholar 

  23. Puri JK, Singh R, Chahal VK (2011) Silatranes: a review on their synthesis, structure, reactivity and applications. Chem Soc Rev 40:1791–1840

    Article  Google Scholar 

  24. Rabenseifner R (1999) Automatic profiling of MPI applications with hardware performance counters. In: Proceedings of the 6th European PVM/MPI users’ group meeting on recent advances in parallel virtual machine and message passing interface. Springer, London, pp 35–42

    Chapter  Google Scholar 

  25. Rountree B, Lowenthal DK, Funk S, Freeh VW, Supinski BR, Schulz M (2007) Bounding energy consumption in large-scale MPI programs. In: Proceedings of the 2007 ACM/IEEE conference on supercomputing (SC ’07), Nov 2007, pp 1–9

    Chapter  Google Scholar 

  26. Rountree B, Lownenthal DK, de Supinski BR, Schulz M, Freeh VW, Bletsch T (2009) Adagio: making DVS practical for complex HPC applications. In: Proceedings of the 23rd international conference on supercomputing (ICS ’09). ACM, New York, pp 460–469

    Chapter  Google Scholar 

  27. Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, Koseki S, Matsunaga N, Nguyen KA, Su S, Windus TL, Dupuis M, Montgomery JA Jr (1993) General atomic and molecular electronic structure system. J Comput Chem 14:1347–1363

    Article  Google Scholar 

  28. Sok S, Gordon MS (2012) A dash of protons: a theoretical study on the hydrolysis mechanism of 1-substituted silatranes and their protonated analogs. Theor Comput Chem 987:2–15

    Article  Google Scholar 

  29. Song S, Ge R, Feng X, Cameron KW (2009) Energy profiling and analysis of the HPC challenge benchmarks. Int J High Perform Comput Appl 23:265–276

    Article  Google Scholar 

  30. Sundriyal V, Sosonkina M (2011) Per-call energy saving strategies in all-to-all communications. In: Proceedings of the 18th European MPI users’ group conference on recent advances in the message passing interface (EuroMPI ’11). Springer, Berlin, pp 188–197

    Chapter  Google Scholar 

  31. Sundriyal V, Sosonkina M, Gaenko A (2012) Runtime procedure for energy savings in applications with point-to-point communications. http://archives.ece.iastate.edu/archive/00000622/

  32. Sundriyal V, Sosonkina M, Zhang Z (2012, in press) Achieving energy efficiency during collective communications. Concurrency and Computation-Practice and Experience

  33. Vishnu A, Song S, Marquez A, Barker K, Kerbyson D, Cameron K, Balaji P (2010) Designing energy efficient communication runtime systems for data centric programming models. In: Proceedings of the 2010 IEEE/ACM int’l conference on green computing and communications & int’l conference on cyber, physical and social computing (GREENCOM-CPSCOM ’10), Washington, DC, USA. IEEE Comput Soc, Los Alamitos, pp 229–236

    Chapter  Google Scholar 

  34. Zamani R, Afsahi A, Qian Y, Hamacher C (2007) A feasibility analysis of power-awareness and energy minimization in modern interconnects for high-performance computing. In: Proceedings of the 2007 international conference on cluster computing, pp 118–128

    Chapter  Google Scholar 

  35. Zhang X, Shen K, Dwarkadas S, Zhong R (2010) An evaluation of per-chip nonuniform frequency scaling on multicores. In: Proceedings of the 2010 USENIX conference on USENIX annual technical conference (USENIXATC’10). USENIX Association, Berkeley, p 19

    Google Scholar 

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Acknowledgements

This work was supported in part by Iowa State University under the contract DE-AC02-07CH11358 with the U.S. Department of Energy, by the Director, Office of Science, Division of Mathematical, Information, and Computational Sciences of the U.S. Department of Energy under contract number DE-AC02-05CH11231, and by the National Science Foundation grants NSF/OCI—0749156, 0941434, 0904782, 1047772. The authors are thankful to the anonymous reviewers for their comments that helped improve the paper.

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Authors and Affiliations

  1. Ames Laboratory, 336 Wilhelm Hall, Ames, USA

    Vaibhav Sundriyal & Masha Sosonkina

  2. Ames Laboratory, 310 Wilhelm Hall, Ames, USA

    Alexander Gaenko

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  1. Vaibhav Sundriyal
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  2. Masha Sosonkina
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Correspondence to Vaibhav Sundriyal.

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Sundriyal, V., Sosonkina, M. & Gaenko, A. Energy efficient communications in quantum chemistry applications. Comput Sci Res Dev 29, 149–158 (2014). https://doi.org/10.1007/s00450-012-0229-x

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