Improving the Research Environment of High Performance Computing for Non-cluster Experts Based on Knoppix Instant Computing Technology

  • Fumikazu Konishi
  • Manabu Ishii
  • Shingo Ohki
  • Yusuke Hamano
  • Shuichi Fukuda
  • Akihiko Konagaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4128)


We have designed and implemented a new portable system that can rapidly construct a computer environment where high-throughput research applications can be performed instantly. One challenge in the instant computing area is constructing a cluster system instantly, and then readily restoring it to its former state. This paper presents an approach for instant computing using Knoppix technology that can allow even a non-computer specialist to easily construct and operate a Beowulf cluster . In the present bio-research field, there is now an urgent need to address the nagging problem posed by having high-performance computers. Therefore, we were assigned the task of proposing a way to build an environment where a cluster computer system can be instantly set up. Through such research, we believe that the technology can be expected to accelerate scientific research. However, when employing this technology in bio-research, a capacity barrier exists when selecting a clustered Knoppix system for a data-driven bioinformatics application. We have approached ways to overcome said barrier by using a virtual integrated RAM-DISK to adapt to a parallel file system. To show an actual example using a reference application, we have chosen InterProScan, which is an integrated application prepared by the European Bioinformatics Institute (EBI) that utilizes many database and scan methods. InterProScan is capable of scaling workload with local computational resources, though biology researchers and even bioinformatics researchers find such extensions difficult to set up. We have achieved the purpose of allowing even researchers who are non-cluster experts to easily build a system of ”Knoppix for the InterProScan4.1 High Throughput Computing Edition.” The system we developed is capable of not only constructing a cluster computer environment composed of 32 computers in about ten minutes (as opposed to six hours when done manually), but also restoring the original environment by rebooting the pre-existing operating system. The goal of our instant cluster computing is to provide an environment in which any target application can be built instantly from anywhere.


High Performance Computing Bioinformatics Application Beowulf Cluster Parallel File System Instant Computing 
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.


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  1. 1.
    Becker, D.J., Sterling, T., Savarese, D., Dorband, J.E., Ranawak, U.A., Packer, C.V.: Beowulf: A Parallel Workstation For Scientific Computation. In: Proceedings, International Conference on Parallel Processing (1995)Google Scholar
  2. 2.
    Sterling, T., Savarese, D., Becker, D.J., Fryxell, B., Olson, K.: Communication Overhead for Space Science Applications on the Beowulf Parallel Workstation. In: Proceedings, High Performance and Distributed Computing, pp. 23–30 (1995)Google Scholar
  3. 3.
    Sterling, T., Becker, D.J., Savarese, D., Berry, M.R., Res, C.: Achieving a Balanced Low-Cost Architecture for Mass Storage Management through Multiple Fast Ethernet Channels on the Beowulf Parallel Workstation. In: Proceedings, International Parallel Processing Symposium, pp. 104–108 (1996)Google Scholar
  4. 4.
    Papadopoulos, P.M., Katz, M.J., Bruno, G.: NPACI Rocks: Tools and Techniques for Easily Deploying Manageable Linux Clusters. In: Cluster 2001: IEEE International Conference on Cluster Computing (2001)Google Scholar
  5. 5.
    Katz, M.J., Papadopoulos, P.M., Bruno, G.: Leveraging Standard Core Technologies to Programmatically Build Linux Cluster Appliances. In: Cluster 2002: IEEE International Conference on Cluster Computing (April 2002)Google Scholar
  6. 6.
    Papadopoulos, P.M., Papadopoulos, C.A., Katz, M.J., Link, W.J., Bruno, G.: Configuring Large High-Performance Clusters at Lightspeed: A Case Study. In: Clusters and Computational Grids for Scientific Computing 2002 (December 2002)Google Scholar
  7. 7.
    Knopper, K.: Building a self-contained autoconfigurarion Linux system on an iso9660 file system. In: 4th Annual Linux Showcase & Conference Atlanta (2000)Google Scholar
  8. 8.
    Barak, A., La’adan, O.: The MOSIX Multicomputer Operating System for High Performance Cluster Computing. Journal of Future Generation Computer Systems (13) 4-5, 361–372 (1998)Google Scholar
  9. 9.
    Amar, L., Barak, A., Shiloh, A.: The MOSIX Parallel I/O System for Scalable I/O Performance. In: Proc. 14-th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2002), Cambridge, MA, November 2002, pp. 495–500 (2002)Google Scholar
  10. 10.
  11. 11.
    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, Atlanta, GA, October 2000, pp. 317–327 (2000)Google Scholar
  12. 12.
  13. 13.
    Zdobnov, E.M., Apweiler, R.: InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17(9), 847–848 (2001)CrossRefGoogle Scholar
  14. 14.
    Mulder, N.J., et al.: InterPro, progress and status in 2005. Nucleic Acids Res. 33, Database Issue:D201-5 (2005)Google Scholar
  15. 15.
    Linux Terminal Server Project,
  16. 16.
    Litzkow, M., Livny, M., Mutka, M.: Condor - A Hunter of Idle Workstations. In: Proceedings of the 8th International Conference of Distributed Computing Systems, June 1988, pp. 104–111 (1988)Google Scholar
  17. 17.
    Tannenbaum, T., Wright, D., Miller, K., Livny, M.: Condor - A Distributed Job Scheduler. In: Sterling, T. (ed.) Beowulf Cluster Computing with Linux, MIT Press, Cambridge (2002)Google Scholar
  18. 18.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)Google Scholar
  19. 19.
    Knoppix for InterProScan4.1 High Throughput Computing Editon,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fumikazu Konishi
    • 1
  • Manabu Ishii
    • 2
  • Shingo Ohki
    • 1
  • Yusuke Hamano
    • 3
  • Shuichi Fukuda
    • 2
  • Akihiko Konagaya
    • 1
  1. 1.Advanced Genome Information Technology Research Group, Bioknowledge Federation Research TeamRIKEN Genomic Science Center (GSC) 
  2. 2.Tokyo Metropolitan Institute of Technology 
  3. 3.VSN Inc 

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