High-performance computing (HPC) is essential for both traditional and emerging scientific fields, enabling scientific activities to make progress. With the development of high-performance computing, it is foreseeable that exascale computing will be put into practice around 2020. As Moore’s law approaches its limit, high-performance computing will face severe challenges when moving from exascale to zettascale, making the next 10 years after 2020 a vital period to develop key HPC techniques. In this study, we discuss the challenges of enabling zettascale computing with respect to both hardware and software. We then present a perspective of future HPC technology evolution and revolution, leading to our main recommendations in support of zettascale computing in the coming future.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Ábrahám E, Bekas C, Brandic I, et al., 2015. Preparing HPC applications for exascale: challenges and recommendations. 18th Int Conf on Network–Based Information Systems, p.401–406.
Asch M, Moore T, Badia R, et al., 2018. Big data and extreme–scale computing: pathways to convergence—toward a shaping strategy for a future software and data ecosystem for scientific inquiry. Int J High Perform Comput Appl, 32(4):435–479. https://doi.org/10.1177.1094342018778123
Cavin RK, Lugli P, Zhirnov VV, 2012. Science and engineering beyond Moore’s law. Proc IEEE, 100:1720–1749. https://doi.org/10.1109/JPROC.2012.2190155
Chong FT, Franklin D, Martonosi M, 2017. Programming languages and compiler design for realistic quantum hardware. Nature, 549(7671):180–187. https://doi.org/10.1038/natur.23459
Diaz J, Muãoz–Caro C, Nião A, 2012. A survey of parallel programming models and tools in the multi and manycore era. IEEE Trans Parall Distrib Syst, 23(8):1369–1386. https://doi.org/10.1109/TPDS.2011.308
Fang J, Varbanescu AL, Sips HJ, 2011. A comprehensive performance comparison of CUDA and OpenCL. Int Conf on Parallel Processing, p.216–225. https://doi.org/10.1109/ICPP.2011.45
Glosli JN, Richards DF, Caspersen KJ, et al., 2007. Extending stability beyond CPU millennium: a micronscale atomistic simulation of Kelvin–Helmholtz instability. ACM/IEEE Conf on Supercomputing, p.1–11. https://doi.org/10.1145/1362622.1362700
Jacob P, Zia A, Erdogan O, et al., 2009. Mitigating memory wall effects in high–clock–rate and multicore CMOS 3D processor memory stacks. Proc IEEE, 97(1):108–122. https://doi.org/10.1109/JPROC.2008.2007472
Jeddeloh J, Keeth B, 2012. Hybrid memory cube new DRAM architecture increases density and performance. Int Symp on VLSI Technology, p.87–88. https://doi.org/10.1109/VLSIT.2012.6242474
Keeton K, 2015. The machine: an architecture for memorycentric computing. 5th Int Workshop on Runtime and Operating Systems for Supercomputers, p.1. https://doi.org/10.1145/2768405.2768406
Kim NS, Chen D, Xiong J, et al., 2017. Heterogeneous computing meets near–memory acceleration and high–level synthesis in the post–Moore era. IEEE Micro, 37(4):10–18. https://doi.org/10.1109/MM.2017.3211105
Kolli A, Rosen J, Diestelhorst S, et al., 2016. Delegated persist ordering. 49th Annual IEEE/ACM Int Symp on Microarchitecture, p.1–13. https://doi.org/10.1109/MICRO.2016.7783761
Lucas R, Ang J, Bergman K, et al., 2014. Top10 exascale research challenges. Department of Energy Office of Science. https://science.energy.gov/~/media/ascr/ascac/pdf/meetings/20140210/Top10reportFEB14.pdf
Mishra S, Chaudhary NK, Singh K, 2013. Overview of optical interconnect technology. Int J Sci Eng Res, 3(4):364–374.
Rumley S, Nikolova D, Hendry R, et al., 2015. Silicon photonics for exascale systems. J Lightw Technol, 33(3):547–562. https://doi.org/10.1109/JLT.2014.2363947
Schroeder B, Gibson GA, 2007. Understanding failures in petascale computers. J Phys, 78(1):012022. https://doi.org/10.1088/1742–6596/78/1.012022
Shen J, Fang J, Sips HJ, et al., 2013. An application–centric evaluation of OpenCL on multi–core CPUs. Parall Comput, 39(12):834–850. https://doi.org/10.1016/j.parco.2013.08.009
Vinaik B, Puri R, 2015. Oracle’s Sonoma processor: advanced low–cost SPARC processor for enterprise workloads. IEEE Hot Chips.27 Symp, p.1–23. https://doi.org/10.1109/HOTCHIPS.2015.7477455
Waldrop MM, 2016. The chips are down for Moore’s law. Nature, 530(7589):144–147. https://doi.org/10.1038/530144a
Wilkes MV, 1995. The memory wall and the CMOS endpoint. SIGARCH Comput Archit News, 23(4):4–6. https://doi.org/10.1145/218864.218865
Wulf WA, McKee SA, 1995. Hitting the memory wall: implications of the obvious. SIGARCH Comput Archit News, 23(1):20–24. https://doi.org/10.1145/216585.216588
Xu W, Lu Y, Li Q, et al., 2014. Hybrid hierarchy storage system in Milkyway–2 supercomputer. Front Comput Sci, 8(3):367–377. https://doi.org/10.1007/s11704–014–3499.6
Xu Z, Chi X, Xiao N, 2016. High–performance computing environment: a review of twenty years of experiments in China. Nat Sci Rev, 3(1):36–48. https://doi.org/10.1093/nsr/nw.001
Zhang P, Fang JB, Tang T, et al., 2018. Auto–tuning streamed applications on Intel Xeon Phi. IEEE Int Parallel and Distributed Processing Symp, p.515–525. https://doi.org/10.1109/IPDPS.2018.00061
Project supported by the National Key Technology R&D Program of China (No. 2016YFB0200401)
About this article
Cite this article
Liao, X., Lu, K., Yang, C. et al. Moving from exascale to zettascale computing: challenges and techniques. Frontiers Inf Technol Electronic Eng 19, 1236–1244 (2018). https://doi.org/10.1631/FITEE.1800494
- High-performance computing
- Storage system
- Manufacturing process
- Programming models and environments