Towards fully adaptive pipeline parallelism for heterogeneous distributed environments

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4330, Issue , 2006, Pages 916-926

  González Vélez, Horacio ^a   Cole, Murray ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER OPERATING SYSTEMS; MUSCULOSKELETAL SYSTEM; PARALLEL PROCESSING SYSTEMS; PIPELINES;

DISTRIBUTED ENVIRONMENTS; LINUX CLUSTERS; PERFORMANCE BOTTLENECKS; PIPELINE PARALLELISMS; PIPELINE STAGES; REMAPPING; RUNTIMES;

PIPELINE PROCESSING SYSTEMS;

EID: 84947709923     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11946441_82     Document Type: Conference Paper

References (20)

1. Pancake, C.M.: Is parallelism for you? IEEE Computat. Sci. Eng. 3 (1996) 18–37
2. Heller, D.: A survey of parallel algorithms in numerical linear algebra. SIAM Review 20 (1978) 740–777
3. Fleury, M., Downton, A.: Pipelined Processor Farms: Structured Design for Embedded Parallel Systems. Wiley-Interscience, New York (2001)
4. Foster, I., Kesselman, C., eds.: The Grid: Blueprint for a new computing infrastructure. Second edn. Morgan Kaufmann, San Francisco (2003)
5. Laforenza, D.: Grid programming: some indications where we are headed. Parallel Comput. 28 (2002) 1733–1752
6. Cole, M.: Algorithmic Skeletons: Structured Management of Parallel Computation. MIT Press, London (1989)
7. González-Vélez, H.: An adaptive skeletal task farm for grids. In: Euro-Par 2005. Number 3648 in Lect. Notes Comput. Sc., Springer-Verlag (2005) 401–410
8. Vadhiyar, S.S., Dongarra, J.: SRS: A framework for developing malleable and migratable parallel applications for distributed systems. Parallel Process. Lett. 13 (2003) 291–312
9. Sievert, O., Casanova, H.: A simple MPI process swapping architecture for iterative applications. Int. J. High Perform. Comput. Appl. 18 (2004) 341–352
10. Longbottom, R.: C/C++ Whetstone benchmark single or double precision. ftp.nosc.mil/pub/aburto (1997) Official version approved by H. J. Curnow on 21/Nov/97.
11. Gunther, N.J.: Analyzing Computer System Performance with Perl: PDQ. Springer-Verlag, Berlin (2004)
12. González, D., Almeida, F., Moreno, L.M., Rodríguez, C.: Towards the automatic optimal mapping of pipeline algorithms. Parallel Comput. 29 (2003) 241–254
13. Banerjee, S., Hamada, T., Chau, P.M., Fellman, R.D.: Macro pipelining based scheduling on high performance heterogeneous multiprocessor systems. IEEE Trans. Acoust. Speech Signal Process. 43 (1995) 1468–1484
14. Chatterjee, S., Strosnider, J.K.: Distributed Pipeline Scheduling: A framework for distributed, heterogeneous real-time system design. Comput. J. 38 (1995) 271–285
15. Vadhiyar, S.S., Dongarra, J.J.: Self adaptivity in grid computing. Concurrency Computat. Pract. Exper. 17 (2005) 235–257
16. Kelly, P.H.J., Beckmann, O.: Generative and adaptive methods in performance programming. Parallel Process. Lett. 15 (2005) 239–255
17. Aldinucci, M., Danelutto, M., Teti, P.: An advanced environment supporting structured parallel programming in Java. Future Gener. Comput. Syst. 19 (2003) 611–626
18. Cole, M.: Bringing skeletons out of the closet: a pragmatic manifesto for skeletal parallel programming. Parallel Comput. 30 (2004) 389–406
19. Yaikhom, G., Cole, M., Gilmore, S.: Combining measurement and stochastic modelling to enhance scheduling decisions for a parallel mean value analysis algorithm. In: ICCS 2006. Number 3992 in Lect. Notes Comput. Sc., Springer-Verlag (2006) 929–936
20. González-Vélez, H.: Self-adaptive skeletal task farm for computational grids. Parallel Comput. (2006) In press doi:10.1016/j.parco.2006.07.002.