Abstract
In the past twenty years, the research group in University of Science and Technology of China has developed an integrated research method for parallel computing, which is a combination of “Architecture-Algorithm-Programming-Application”. This method is also called the ecological environment of parallel computing research. In this paper, we survey the current status of integrated research method for parallel computing and by combining the impact of multi-core systems, cloud computing and personal high performance computer, we present our outlook on the future development of parallel computing.
Similar content being viewed by others
References
Chen G L, Sun G Z, Zhang Y Q, et al. Study on parallel computing. J Comput Sci Tech, 2006, 21(5): 665–673
Chen G L. Parallel Computing — Architecture·Algorithm·Programming. Beijing: Higher Education Press, 2003
Grama A, Gupta A, Karypis G, et al. Introduction to parallel computing. Boston: Benjaming/Cummings Publish Company, Inc., 2003
Chen G L. Design and Analysis of Parallel Algorithms. Beijing: Higher Education Press, 2002
Chen G L, Sun G Z, Xu Y, et al. Methodology of research on parallel algorithms. J Chin Comput, 2008, 31(9): 1493–1502
Chen G L. A partitioning selection algorithm on multiprocessors. J Comput Sci Tech, 1988, 3(4): 241–250
Chen G L, Liang W F, Shen H, Research advances in parallel graph algorithms. J Comput Res & Dev, 1995, 32(9): 1–16
An H, Chen G L. Parallel programming models and languages. J Sof, 2002, 13(1): 118–124
Mao R, Huang L S, Xu D J, et al. Algorithm and implementation of multi-reservoir optimal scheduling on Huaihe River. Mini-Micro Sys, 2000, 21(6): 44–48
Zhang F, Chen G L, Zhang Z Q. OpenMP on networks of workstations for software DSMs. J Comput Sci Tech, 2002, 17(1): 90–100
Sutter H, Larus J. Software and the concurrency revolution. Q Focus: Multiprocessors, 2005, 3(7): 54–62
Rajkumar B, Chee S Y, Srikumar V. Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities. In: Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications, 2008 Sept 25–27, Dalian. Los Alamitos, CA: IEEE CS Press, 2008, 15–22
Sun N H, Chen G L. PHPC: A spreading king of high performance computer. J China Univ Sci Tech, 2008, 38(7): 745–752
Zhang J X, Zhang H J, Li H M. Design of tera-flops High-Performance computer KD-50-I based on Loongson-2F CPU. J Chin Univ Sci Tech, 2008, 38(1): 105–108
Asanovic K, Bodik R, James J, et al. The Landscape of Parallel Computing Research: A View from Berkeley. Technical Report, Electrical Engineering and Computer Sciences, University of California, Berkeley. 2006
Zhang Y Q, Chen G L, Sun G Z, et al. Models of parallel computation: a survey and classification. Front Comput Sci Chin, 2007, 1(2): 156–165
Chen G L, Miao Q K, Sun G Z, et al. Layered models of parallel computation. J Chin Univ Sci Tech, 2008, 38(7): 841–847
Sun X H. Scalable Computing in the multi-core era, In: Proceedings of the Inaugural Symposium on Parallel Algorithms, Architechures and Programming, 2008 Sep 16–18, Hefei. Hefei: University of Science and Technology of China Press, 2008. 1–18
Chu C T, Yu Y Y, Bradski G, et al. Map-Reduce for Machine Learning on Multicore. In: Twentieth Annual Conference on Neural Information Processing Systems, 2006, Dec 4–7, Vancouver. Boston: MIT Press, 2006. 281–288
Mattson T G, Sanders B A, Massingill B L. Patterns for Parallel Programming. New Jersey: Prentice Hall, 2005
Dongarra J, Fox G, Kennedy K, et al. Sourcebook of Parallel Computing. San Francisco: Morgan Kaufmann Publishers Inc., 2003
Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Sixth Symposium on Operating System Design and Implementation, 2004, Dec 6–8, San Francisco, CA. Berkeley: USENIX Association, 2004. 10–23
Ghemawat S, Gobioff H, Shun-Tak L. The Google file system. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems principles, 2003 Oct 19–22, New York. New York: ACM Press, 2003. 29–43
Chang F, Dean J, Ghemawat S, et al. Bigtable: A distributed storage system for structured data. In: Seventh Symposium on Operating System Design and Implementation, 2006, Nov 6–8, Seattle. Berkeley: USENIX Association, 2006. 205–218
Isard M, Yu Y, Birrell A, et al. Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks. Technical Report, Microsoft Research Technical Report, Microsoft Corporation, 2006
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (Grant Nos. 60533020 and 60873210)
About this article
Cite this article
Chen, G., Sun, G., Xu, Y. et al. Integrated research of parallel computing: Status and future. Chin. Sci. Bull. 54, 1845–1853 (2009). https://doi.org/10.1007/s11434-009-0261-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-009-0261-9