Abstract
On June 17, 2013, MilkyWay-2 (Tianhe-2) supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity-off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16-core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.
Similar content being viewed by others
References
Yang X J, Liao X K, Lu K, Hu Q F, Song J Q, Su J S. The Tianhe-1a supercomputer: its hardware and software. Journal of Computer Science and Technology, 2011, 26(3): 344–351
Zhang H, Wang K, Zhang J, Wu N, Dai Y. A fast and fair shared buffer for high-radix router. Journal of Circuits, Systems, and Computers, 2013
Kirk D. Nvidia cuda software and GPU parallel computing architecture. In: Proceedings of the 6th International Symposium on Memory Management. 2007, 103–104
Sherlekar S. Tutorial: Intel many integrated core (MIC) architecture. In: Proceedings of the 18th IEEE International Conference on Parallel and Distributed Systems. 2012, 947
Gaster B, Howes L, Kaeli D R, Mistry P, Schaa D. Heterogeneous Computing with OpenCL. Morgan Kaufmann Publishers Inc., 2011
Lee S, Vetter J S. Early evaluation of directive-based GPU programming models for productive exascale computing. In: Proceedings of the 2012 International Conference for High Performance Computing, Networking, Storage and Analysis. 2012, 1–11
Wienke S, Springer P, Terboven C, Mey D. Openacc: first experiences with real-world applications. In: Proceedings of the 18th International Conference on Parallel Processing. 2012, 859–870
PGI Accelerator Compilers. Portland Group Inc, 2011
Yang X L, Tang T, Wang G B, Jia J, Xu X H. MPtoStream: an openMP compiler for CPU-GPU heterogeneous parallel systems. Science China Information Sciences, 2012, 55(9): 1961–1971
Dolbeau R, Bihan S, Bodin F. Hmpp: a hybrid multi-core parallel programming environment. In: Proceedings of the 2007 Workshop on General Purpose Processing on Graphics Processing Units. 2007, 1–5
Checconi F, Petrini F, Willcock J, Lumsdaine A, Choudhury A R, Sabharwal Y. Breaking the speed and scalability barriers for graph exploration on distributed-memory machines. In: Proceedings of the 2012 International Conference for High Performance Computing, Networking, Storage and Analysis. 2012, 1–12
Beamer S, Buluç A, Asanovic K, Patterson D. Distributed memory breadth-first search revisited: enabling bottom-up search. In: Proceedings of the 27th IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum. 2013, 1618–1627
Subramaniam S, Mehrotra M, Gupta D. Virtual high throughput screening (VHIS)-a perspective. Bioinformation, 2007, 3(1): 14–17
Tanrikulu Y, Krüger B, Proschak E. The holistic integration of virtual screening in drug discovery. Drug Discovery Today, 2013, 18(7): 358–364
Zhang X, Wong S E, Lightstone F C. Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines. Journal of Computational Chemistry, 2013, 34(11): 915–927
Lang P T, Brozell S R, Mukherjee S, Pettersen E F, Meng E C, Thomas V, Rizzo R C, Case D A, James T L, Kuntz I D. Dock 6: combining techniques to model RNA-small molecule complexes. RNA, 2009, 15(6): 1219–1230
Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H. PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics, 2008, 9(1): 104
Yang C, Xue W, Fu H, Gan L, Li L, Xu Y, Lu Y, Sun J, Yang G, Zheng W. A peta-scalable CPU-GPU algorithm for global atmospheric simulations. In: Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 2013, 1–12
Author information
Authors and Affiliations
Corresponding author
Additional information
Xiangke Liao received the BS in computer science from Tsinghua University, China and MS degree in computer science from the National University of Defense Technology (NUDT), China. Currently he is a professor at NUDT. His research interests include high performance computing system, operating system, parallel software. He is the chief designer of MilkyWay-2 system.
Liquan Xiao received his MS and PhD in computer science from National University of Defense Technology (NUDT), China. Currently he is a professor at the university. His research interests include architecture of high performance computing, high speed interconnect network, system integration, and power management. He is a deputy chief designer of MilkyWay-2 supercomputer.
Canqun Yang received his MS and PhD in computer science from National University of Defense Technology (NUDT), China in 1995 and 2008, respectively. Currently he is a professor at the university. His research interests include programming languages and compiler implementation. He is a director designer of the MilkyWay-2 supercomputer.
Yutong Lu received her MS and PhD in computer science from National University of Defense Technology (NUDT), China. Currently she is a professor at the university. Her research interests include parallel system management, high speed communication, distributed file systems, and advanced programming environments with MPI. She is a director designer of MilkyWay-2 supercomputer.
Rights and permissions
About this article
Cite this article
Liao, X., Xiao, L., Yang, C. et al. MilkyWay-2 supercomputer: system and application. Front. Comput. Sci. 8, 345–356 (2014). https://doi.org/10.1007/s11704-014-3501-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-014-3501-3