Advertisement

Bridging HPC and Grid File I/O with IOFSL

  • Jason Cope
  • Kamil Iskra
  • Dries Kimpe
  • Robert Ross
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7134)

Abstract

Traditionally, little interaction has taken place between the Grid and high-performance computing (HPC) storage research communities. Grid research often focused on optimizing data accesses for high-latency, wide-area networks, while HPC research focused on optimizing data accesses for local, high-performance storage systems. Recent software and hardware trends are blurring the distinction between Grids and HPC. In this paper, we investigate the use of I/O forwarding — a well established technique in leadership-class HPC machines— in a Grid context. We show that the problems that triggered the introduction of I/O forwarding for HPC systems also apply to contemporary Grid computing environments. We present the design of our I/O forwarding infrastructure for Grid computing environments. Moreover, we discuss the advantages our infrastructure provides for Grids, such as simplified application data management in heterogeneous computing environments and support for multiple application I/O interfaces.

Keywords

Argonne National Laboratory Grid Resource Grid Environment Remote Data Grid Computing Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ali, N., Carns, P., Iskra, K., Kimpe, D., Lang, S., Latham, R., Ross, R., Ward, L., Sadayappan, P.: Scalable I/O forwarding framework for high-performance computing systems. In: IEEE Int’l Conference on Cluster Computing (Cluster 2009) (September 2009)Google Scholar
  2. 2.
    Allen, G., Dramlitsch, T., Foster, I., Karonis, N., Ripeanu, M., Seidel, E., Toonen, B.: Supporting efficient execution in heterogeneous distributed computing environments with cactus and globus. In: Proceedings of SC 2001, November 10-16 (2001)Google Scholar
  3. 3.
    Baer, T., Wyckoff, P.: A parallel I/O mechanism for distributed systems. In: IEE Cluster 2004, pp. 63–69. IEEE (2004)Google Scholar
  4. 4.
    Carns, P.H., Ligon III, W.B., Ross, R.B., Thakur, R.: PVFS: A parallel file system for Linux clusters. In: Proceedings of the 4th Annual Linux Showcase and Conference, pp. 317–327 (2000)Google Scholar
  5. 5.
    Cluster File Systems, Inc.: Lustre: A scalable high-performance file system. Tech. rep., Cluster File Systems (November 2002), http://www.lustre.org/docs/whitepaper.pdf
  6. 6.
    Grinberg, L., Karniadakis, G.: A scalable domain decomposition method for ultra-parallel arterial flow simulations. Communications in Computational Physics 4(5), 1151–1169 (2008)Google Scholar
  7. 7.
    Iskra, K., Romein, J.W., Yoshii, K., Beckman, P.: ZOID: I/O-forwarding infrastructure for petascale architectures. In: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Salt Lake City, UT, pp. 153–162 (2008)Google Scholar
  8. 8.
    Karonis, N., Toonen, B., Foster, I.: MPICH-G2: A Grid-enabled implementation of the Message Passing Interface. Journal of Parallel and Distributed Computing 63(5), 551–563 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kelly, S., Brightwell, R.: Software architecture of the light weight kernel, Catamount. In: Proceedings of the 2005 Cray User Group Annual Technical Conference (2005)Google Scholar
  10. 10.
    Kosar, T., Balman, M.: A new paradigm: Data-aware scheduling in grid computing. Future Generation Computer Systems 25(4), 406–413 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, J., Liao, W., Choudhary, A., Ross, R., Thakur, R., Gropp, W., Latham, R., Siegel, A., Gallagher, B., Zingale, M.: Parallel netCDF: A high-performance scientific I/O interface. In: ACM/IEEE Conference on Supercomputing, Phoenix, AZ (November 2003)Google Scholar
  12. 12.
    Nagle, D., Serenyi, D., Matthews, A.: The Panasas ActiveScale storage cluster—delivering scalable high bandwidth storage. In: ACM/IEEE Conference on Supercomputing (November 2004)Google Scholar
  13. 13.
    Nowoczynski, P., Stone, N., Yanovich, J., Sommerfield, J.: Zest: Checkpoint storage system for large supercomputers. In: 3rd Petascale Data Storage Workshop, IEEE Supercomputing, pp. 1–5 (November 2008)Google Scholar
  14. 14.
    Ohta, K., Kimpe, D., Cope, J., Iskra, K., Ross, R., Ishikawa, Y.: Optimization Techniques at the I/O Forwarding Layer. In: IEEE Int’l Conference on Cluster Computing (Cluster 2010) (September 2010)Google Scholar
  15. 15.
    Schmuck, F., Haskin, R.: GPFS: A shared-disk file system for large computing clusters. In: USENIX Conference on File and Storage Technologies (2002)Google Scholar
  16. 16.
    Tatebe, O., Morita, Y., Matsuoka, S., Soda, N., Sekiguchi, S.: Grid datafarm architecture for petascale data intensive computing. In: CCGrid 2002, p. 102. IEEE Computer Society (2002)Google Scholar
  17. 17.
    Tatebe, O., Soda, N., Morita, Y., Matsuoka, S., Sekiguchi, S.: Gfarm v2: A Grid file system that supports high-performance distributed and parallel data computing. In: Proceedings of the Computing in High Energy and Nuclear Physics Conference, CHEP 2004 (2004)Google Scholar
  18. 18.
    Thain, D., Moretti, C., Hemmes, J.: Chirp: A practical global filesystem for cluster and Grid computing. Journal of Grid Computing 7(1), 51–72 (2009)CrossRefGoogle Scholar
  19. 19.
    Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: The Condor experience. Concurrency and Computation Practice and Experience 17(2-4), 323–356 (2005)CrossRefGoogle Scholar
  20. 20.
    Wilde, M., Ioan Raicu, I., Espinosa, A., Zhang, Z., Clifford, B., Hategan, M., Kenny, S., Iskra, K., Beckman, P., Foster, I.: Extreme-scale scripting: Opportunities for large task-parallel applications on petascale computers. Journal of Physics: Conference Series 180(1) (2009)Google Scholar
  21. 21.
    Yu, H., Sahoo, R.K., Howson, C., Almasi, G., Castanos, J.G., Gupta, M., Moreira, J.E., Parker, J.J., Engelsiepen, T.E., Ross, R., Thakur, R., Latham, R., Gropp, W.D.: High performance file I/O for the Blue Gene/L supercomputer. In: International Symposium on High-Performance Computer Architecture (February 2006)Google Scholar
  22. 22.
    Zheng, F., Abbasi, M., Docan, C., Lofstead, J., Liu, Q., Klasky, S., Prashar, M., Podhorszki, N., Schwan, K., Wolf, M.: Predata - preparatory data analytics on peta-scale machines. In: Proceedings of 24th IEEE International Parallel and Distributed Processing Symposium (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jason Cope
    • 1
  • Kamil Iskra
    • 1
  • Dries Kimpe
    • 2
  • Robert Ross
    • 1
  1. 1.Mathematics and Computer Science DivisionArgonne National LaboratoryUSA
  2. 2.Computation InstituteUniversity of Chicago / Argonne National LaboratoryUSA

Personalised recommendations