Skip to main content
SpringerLink
Log in

Extreme-scale parallel computing: bottlenecks and strategies

  • Perspective
  • Published:
Frontiers of Information Technology & Electronic Engineering Aims and scope Submit manuscript

Abstract

Extreme-scale numerical simulations seriously demand extreme parallel computing capabilities. To address the challenges of these capabilities toward exascale, we systematically analyze the major bottlenecks of parallel computing research from three perspectives: computational scale, computing efficiency, and programming productivity. For these bottlenecks, we propose a series of urgent key issues and coping strategies. This study will be useful in synchronizing development between the numerical computing capability and supercomputer peak performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amarasinghe S, Hall M, Lethin R, et al., 2011. Exascale programming challenges. Technical Report of the Workshop on Exascale Programming Challenges.

    Google Scholar 

  • Ashby S, Beckman P, Chen J, et al., 2011. The opportunities and challenges of exascale computing. Summary Report of the Advanced Scientific Computing Advisory Committee Subcommittee.

    Google Scholar 

  • Balay S, Gropp WD, McInnes LC, et al., 1997. Efficient management of parallelism in object–oriented numerical software libraries. In: Arge E, Bruaset AM, Langtangen HP (Eds.), Modern Software Tools for Scientific Computing. Birkhauser Boston Inc., Cambridge, USA. https://doi.org/10.1007/978–1–4612–1986–6.8

  • Campos C, Roman JE, 2012. Strategies for spectrum slicing based on restarted Lanczos methods. Numer Algor, 60(2):279–295. https://doi.org/10.1007/s11075–012–9564–z

    Article  MathSciNet  MATH  Google Scholar 

  • Cao X, Mo Z, Liu X, et al., 2011. Parallel implementation of fast multipole method based on JASMIN. Sci China Inform Sci, 54(4):757–766 (in Chinese). https://doi.org/10.1007/s11432–011–4181.3

    Article  MathSciNet  Google Scholar 

  • Chung IH, Lee CR, Zhou J, et al., 2011. Hierarchical mapping for HPC applications. IEEE Int Symp on Parallel and Distributed Processing Workshops and PhD Forum, p.1815–1823. https://doi.org/10.1109/IPDPS.2011.340

    MATH  Google Scholar 

  • Cooley JW, Tukey JW, 1965. An algorithm for the machine calculation of complex Fourier series. Math Comput, 19(90):297–301. https://doi.org/10.1090/S0025–5718–1965–0178586.1

    Article  MathSciNet  MATH  Google Scholar 

  • Darve E, 2000. The fast multipole method: numerical implementation. J Comput Phys, 160(1):195–240. https://doi.org/10.1006/jcph.2000.6451

    Article  MathSciNet  MATH  Google Scholar 

  • Dolean V, Jolivet P, Nataf F, 2015. An Introduction to Domain Decomposition Methods: Algorithms, Theory, and Parallel Implementation. Society for Industrial and Applied Mathematics, Philadelphia, USA. https://doi.org/10.1137/1.9781611974065

    Book  MATH  Google Scholar 

  • Dongarra J, Foster I, Fox G, et al., 2003. The Sourcebook of Parallel Computing. Morgan Kaufmann Publishers Inc., San Francisco, USA.

    Google Scholar 

  • Dubey A, Almgren A, Bell J, et al., 2014. A survey of high level frameworks in block–structured adaptive mesh refinement packages. J Parall Distr Comput, 74(12):3217–3227. https://doi.org/10.1016/j.jpdc.2014.07.001

    Article  Google Scholar 

  • Engheta N, Murphy WD, Rokhlin V, et al., 1992. The fast multipole method (FMM) for electromagnetic scattering problems. IEEE Trans Antenn Propag, 40(6):634–641. https://doi.org/10.1109/8.144597

    Article  MathSciNet  MATH  Google Scholar 

  • Falgout RD, Yang UM, 2002. Hypre: a library of high performance pre–conditioners. Int Conf on Computational Science, p.632–641.

    MATH  Google Scholar 

  • Fu H, He C, Chen B, et al., 2017. 18.9–Pflops nonlinear earthquake simulation on Sunway TaihuLight: enabling depiction of 18–Hz and 8–meter scenarios. Int Conf for High Performance Computing, Networking, Storage, and Analysis, p.1–12. https://doi.org/10.1145/3126908.3126910

    Google Scholar 

  • Hennessy JL, Patterson DA, 2003. Computer Architecture: a Quantitative Approach. Morgan Kaufmann Publishers Inc., San Francisco, USA.

    Google Scholar 

  • Hernandez V, Roman JE, Vidal V, 2005. SLEPc: a scalable and flexible toolkit for the solution of eigenvalue problems. ACM Trans Math Softw, 31(3):351–362. https://doi.org/10.1145/1089014.1089019

    Article  MathSciNet  MATH  Google Scholar 

  • Heroux MA, Bartlett RA, Howle VE, et al., 2005. An overview of the Trilinos project. ACM Trans Math Softw, 31(3):397–423. https://doi.org/10.1145/1089014.1089021

    Article  MathSciNet  MATH  Google Scholar 

  • Johansen H, McInnes LC, Bernholdt DE, et al., 2014. Software productivity for extreme–scale science. DOE Workshop Report.

    Google Scholar 

  • Keyes DE, Mcinnes LC, Woodward CS, et al., 2013. Multiphysics simulations: challenges and opportunities. Int J High Perform Comput Appl, 27(1):4–83. https://doi.org/10.1177.1094342012468181

    Article  Google Scholar 

  • Knoll DA, Keyes DE, 2004. Jacobian–free Newton–Krylov methods: a survey of approaches and applications. J Comput Phys, 193(2):357–397. https://doi.org/10.1016/j.jcp.2003.08.010

    Article  MathSciNet  MATH  Google Scholar 

  • Li J, Zhang X, Tan G, et al., 2013. SMAT: an input adaptive sparse matrix–vector multiplication auto–tuner. ACM SIGPLAN Not, 48(6):117–126. https://doi.org/10.1145/2499370.2462181

    Article  Google Scholar 

  • Liu X, Yang Z, Yang Y, 2018. A nested partitioning load balancing algorithm for Tianhe–2. J Comput Res Devel, 55(2):418–425. https://doi.org/10.7544/issn1000–1239.2018.20160877

    Google Scholar 

  • Lucas R, Ang J, Bergman K, et al., 2014. DOE Advanced Scientific Computing Advisory Subcommittee report: top 10 exascale research challenges. https://doi.org/10.2172.1222713

    Book  Google Scholar 

  • Mo Z, 2014. Domain–specific programming model for high performance scientific and engineering computation. Commun CCF, 10(1):8–12 (in Chinese).

    Google Scholar 

  • Mo Z, 2015. Progress on high performance programming framework for numerical simulation. E–Sci Technol Appl, 6(4):11–19 (in Chinese). https://doi.org/10.11871/j.issn.1674–9480.2015.04.002

    Google Scholar 

  • Mo Z, 2016. High performance programming frameworks for numerical simulation. Nat Sci Rev, 3(1):28–29. https://doi.org/10.1093/nsr/nw.086

    Article  Google Scholar 

  • Mo Z, Zhang A, Cao X, et al., 2010. JASMIN: a parallel software infrastructure for scientific computing. Front Comput Sci China, 4(4):480–488. https://doi.org/10.1007/s11704–010–0120.5

    Article  Google Scholar 

  • Mo Z, Zhang A, Liu Q, et al., 2015. Research on the components and practices for domain–specific parallel programming models for numerical simulation. Sci Sin Inform, 45(3):385–397 (in Chinese). https://doi.org/10.1360/N112013.00197

    Article  Google Scholar 

  • Mo Z, Zhang A, Liu Q, et al., 2016. Parallel algorithm and parallel programming: from specialty to generality as well as software reuse. Sci Sin Inform, 46(10):1392–1410 (in Chinese). https://doi.org/10.1360/N112016.00144

    Google Scholar 

  • Pei W, Zhu S, 2009. Scientific computing for laser fusion. Physics, 38(8):559–568 (in Chinese). https://doi.org/10.3321/j.issn:0379–4148.2009.08.005

    Google Scholar 

  • Reed DA, Bajcsy R, Fernandez MA, et al., 2005. Computational science: ensuring America’s competitiveness. Research Report No. ADA462840. President’s Information Technology Advisory Committee. http://www.dtic.mil/dtic/tr/fulltext/u2/a462840.pdf

    Google Scholar 

  • Rossinelli D, Hejazialhosseini B, Hadjidoukas P, et al., 2013 11 Pflop/s simulations of cloud cavitation collapse. Int Conf on High Performance Computing, Networking, Storage, and Analysis, p.1–13. https://doi.org/10.1145/2503210.2504565

    Google Scholar 

  • Rudi J, Malossi ACI, Isaac T, et al., 2015. An extreme–scale implicit solver for complex PDEs: highly heterogeneous flow in Earth’s mantle. Int Conf for High Performance Computing, Networking, Storage, and Analysis, p.1–12. https://doi.org/10.1145/2807591.2807675

    Book  Google Scholar 

  • Saad T, Darwish M, 2009. A high scalability parallel algebraic multigrid solver. In: Deconinck H, Dick E (Eds.), Computational Fluid Dynamics. Springer Berlin Heidelberg, p.231–236. https://doi.org/10.1007/978–3–540–92779–2.34

  • Saad Y, 2003. Iterative Methods for Sparse Linear Systems (2nd Ed.). Society for Industrial and Applied Mathematics, Philadelphia, USA.

    Book  MATH  Google Scholar 

  • Sarkar V, Budimlic Z, Kulkani M, 2016. 2014 runtime systems Summit. Runtime Systems Report. https://doi.org/10.2172.1341724

    Book  Google Scholar 

  • Shaw DE, Grossman JP, Bank JA, et al., 2014. Anton 2: raising the bar for performance and programmability in a special–purpose molecular dynamics supercomputer. Int Conf for High Performance Computing, Networking, Storage, and Analysis, p.41–53. https://doi.org/10.1109/SC.2014.9

    Google Scholar 

  • Tian R, Zhou M, Wang J, et al., 2018. A challenging dam structural analysis: large–scale implicit thermomechanical coupled contact simulation on Tianhe–2. Comput Mech, p.1–21. https://doi.org/10.1007/s00466–018–1586.5

    Google Scholar 

  • Vuduc R, Demmel JW, Yelick KA, 2005. OSKI: a library of automatically tuned sparse matrix kernels. J Phys Conf Ser, 16:521–530. https://doi.org/10.1088/1742–6596/16/1.071

    Article  Google Scholar 

  • Wissink AM, Hornung RD, Kohn SR, et al., 2001. Large scale parallel structured AMR calculations using the SAMRAI framework. ACM/IEEE Conf on Supercomputing, p.6. https://doi.org/10.1145/582034.582040

    Book  Google Scholar 

  • Xu X, Mo Z, 2017. Algebraic interface–based coarsening AMG pre–conditioner for multi–scale sparse matrices with applications to radiation hydrodynamics computation. Numer Linear Algebra Appl, 24(2):e2078. https://doi.org/10.1002/nla.2078

    Article  MATH  Google Scholar 

  • Yang C, Xue W, Fu H, et al., 2016. 10M–core scalable fullyimplicit solver for non–hydrostatic atmospheric dynamics. Int Conf for High Performance Computing, Networking, Storage, and Analysis, p.1–12. https://doi.org/10.1109/SC.2016.5

    Google Scholar 

  • Yang X, 2012. Sixty years of parallel computing. Comput Eng Sci, 34(8):1–10 (in Chinese). https://doi.org/10.3969/j.issn.1007–130X.2012.08.001

    Google Scholar 

  • Zhao Z, Zhou H, Ma H, et al., 2014. Numerical simulation and verification of electromagnetic pulse effect of PIN diode limiter. High Power Laser Particle Beams, 26(6):81–85 (in Chinese). https://doi.org/10.11884/HPLPB201426.063018

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088, China

    Ze-yao Mo

  2. Institute of Applied Physics and Computational Mathematics, Beijing, 100094, China

    Ze-yao Mo

Authors
  1. Ze-yao Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ze-yao Mo.

Additional information

Project supported by the National Natural Science Foundation of China (No. 91430218) and the National Key Technology R&D Program of China (Nos. 2016YFB0201300 and 2017YFB0202103)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, Zy. Extreme-scale parallel computing: bottlenecks and strategies. Frontiers Inf Technol Electronic Eng 19, 1251–1260 (2018). https://doi.org/10.1631/FITEE.1800421

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/FITEE.1800421

Key words

CLC number

Search

Navigation