|
|
|
Four styles of parallel and net programming |
| Zhiwei XU 1, Li ZHA 1, Yongqiang HE 2, Wei LIN 2, |
| 1.Institute of Computing
Technology, Chinese Academy of Sciences, Beijing 100190, China; 2.Institute of Computing
Technology, Chinese Academy of Sciences, Beijing 100190, China；Graduate School
of the Chinese Academy of Sciences, Beijing 100039, China; |
|
|
|
|
Abstract This paper reviews the programming landscape for parallel and network computing systems, focusing on four styles of concurrent programming models, and example languages/libraries. The four styles correspond to four scales of the targeted systems. At the smallest coprocessor scale, Single Instruction Multiple Thread (SIMT) and Compute Unified Device Architecture (CUDA) are considered. Transactional memory is discussed at the multicore or process scale. The MapReduce style is examined at the datacenter scale. At the Internet scale, Grid Service Markup Language (GSML) is reviewed, which intends to integrate resources distributed across multiple datacenters. The four styles are concerned with and emphasize different issues, which are needed by systems at different scales. This paper discusses issues related to efficiency, ease of use, and expressiveness.
|
| Keywords
concurrent programming
CUDA
SIMT
transactional memory
MapReduce
GSML
|
|
Issue Date: 05 September 2009
|
|
|
Bryant R. Data-intensivesupercomputing: the case for DISC. TechnicalReport CMU-CS-07-128, Carnegie Mellon University, 2007
|
|
Chaiken R, Jenkins B, Larson P, et al. SCOPE: easy and efficient parallel processingof massive data sets. In: InternationalConference of Very Large Data Bases (VLDB). VLDB Endowment, 2008, 1265―1276
|
|
Cooper B, Ramakrishnan R, et al. PNUTS: Yahoo!’shosted data serving platform. In: InternationalConference of Very Large Data Bases (VLDB). VLDB Endowment, 2008, 1277―1278
|
|
Dean J, Ghemawat S. MapReduce: simplified dataprocessing on large clusters. In: Proceedingsof the 6th Symposium on Operating Systems Design and Implementation.USENIX Association, 2004, 137―150
|
|
Garland M, Grand S, Nickolls J, et al. Parallel computing experiences with CUDA. IEEE Micro, 2008, 28(4): 13―27
doi: 10.1109/MM.2008.57
|
|
Guerraoui R, Herlihy M, Pochon B. Polymorphic contention management. In: Proceedings of the 19th International Symposium on DistributedComputing. New York: Springer Verlag, 2005, 303―323
|
|
Harris T, Fraser T. Language support for lightweighttransactions. In: Proceedings of the 8thAnnual ACM SIGPLAN Conference on Object-oriented Programming, Systems,Languages, and Applications. NewYork: ACM Press, 2003, 388―402
|
|
Harris T, Marlow S, Peyton-Jones S, et al. Composable memory transactions. In: Proceedings of the 10th ACM SIGPLAN Symposium on Principles andPractice of Parallel Programming. New York: ACM Press, 2005, 48―60
|
|
Herlihy M, Eliot J, Moss B. Transactional memory: architectural support for lock-freedata structures. In: Proceedings of the20th Annual International Symposium on Computer Architecture. New York: ACM Press, 1993, 289―300
|
|
Herlihy M, Luchangco V, Moir M, Scherer III W N. Software transactional memory for dynamic-sized data structures. In: Proceedings of the 22nd Annual Symposium onPrinciples of Distributed Computing. New York: ACM Press, 2003, 92―101
|
|
Herlihy M, Nir Shavit. The art of multiprocessorprogramming. Morgan Kaufmann, 2008
|
|
Hwang K. Xu Z. Scalable parallel computing:technology, architecture, programming. McGraw-Hill Science/Engineering/Math, 1998
|
|
Khronos Group. TheOpenCL specification, version 1.0. 12/08/2008
|
|
Larus J, Kozyrakis C. Transactional memory. New York: Communication of the ACM, 2008, 51(7): 80―88
doi: 10.1145/1364782.1364800
|
|
Larus R, Rajwar R. Transactional memory. Morgan & Claypool, 2006
|
|
Liu X, Radenac Y, Ban atre J, et al. A chemical interpretation of GSML programs. In: 7th International Conference on Grid and CooperativeComputing(GCC2008). 2008, 459―466
|
|
Minh C, Trautmann M, Chung J, et al. An effective hybrid transactional memory systemwith strong isolation guarantees. In: Proceedingsof the 34th International Symposium on Computer Architecture. New York: ACM Press, 2007, 69―80
|
|
NVIDIA. Corp. NVIDIACUDA programming guide, version 2.0. 06/07/2008
|
|
Owens J, Luebke D, et al. A survey of general-purposecomputation on graphics hardware. ComputerGraphics Forum. 2007, 26(1): 80―113
doi: 10.1111/j.1467-8659.2007.01012.x
|
|
Patterson D. Thedata center is the computer. New York: Communication of the ACM, 2008, 51(1): 105―105
doi: 10.1145/1327452.1327491
|
|
Ryoo S, Rodrigues C, Baghsorkhi S, et al. Optimization principles and application performanceevaluation of a multithreaded GPU using CUDA. In: Proceedings of the 13th Symposium on Principles and Practiceof Parallel Programming. New York: ACMPress, 2008, 73―82
|
|
Saha B, Adl-Tabatabai R, Jacobson Q. Architectural support for software transactional memory. In: Proceedings of the 39th International Symposiumon Microarchitecture. 2006, 185―196
|
|
Scherer III W N, Scott M L. Advanced contention managementfor dynamic software transactional memory. In: Proceedings of the 24th Annual ACM SIGACT-SIGOPS Symposium onPrinciples of Distributed Computing. New York: ACM Press, 2005, 240―248
|
|
Shavit N, Touitou D. Software transactional memory. In: Proceedings of the 14th ACM Symposium on Principlesof Distributed Computing. NewYork: ACM Press, 1995, 204―213
|
|
Shu C, Yu H, Liu H. Beap: an end-user agile programming paradigm for businessapplications. Journal of Computer Scienceand Technology, 2006, 21(4): 609―619
doi: 10.1007/s11390-006-0609-4
|
|
Tarditi D, Puri S, et al. Accelerator: usingdata parallelism to program GPUs for general-purpose uses. In: Proceedings of the 12th International Conferenceon Architectural Support for Programming Languages and Operating Systems. 2006, 325―335
|
|
Volkov V, Demmel J. Benchmarking GPUs to tunedense linear algebra. In: Proceedings ofConference on Supercomputing. IEEE Press, 2008
|
|
Zaharia M, Konwinski A, Joseph A, et al. Improving MapReduce performance in heterogeneousenvironments. In: 8th Symposium on OperatingSystems Design and Implementation. USENIX Association, 2008
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
