OSCAR and Bioinformatics
The OSCAR (Open Source Cluster Application Resources) Project has been around for about four years. The concept initially was proposed in January 2000, and the first organizational meeting was held in April of the same year. The group acknowledged that cluster assembly is time consuming and repetitive. Thus, the project's goal was to create a toolkit to automate this process. In doing so, the group hoped to broaden the usage of clusters and adapt them for the academic and private sector.
The OSCAR Project is overseen by the advisory group OCG (Open Cluster Group), an informal group with open membership. The OCG strives to make cluster computing more practical for high-performance computing (HPC) research and development. The group, like the OSCAR Project, is directed by representatives from research/academia as well as industry. Key players of the group include Bald Guy Software, BC Genome Sciences Centre, Dell, Indiana University, Intel, Louisiana Tech University, Oak Ridge National Laboratory, Revolution Linux and Sherbrooke University.
OSCAR is one of the OCG working groups. Other projects include HA-OSCAR (high-availability), Thin-OSCAR (diskless) and SSS-OSCAR (Scalable Systems Software). To learn more about OCG and its projects, see the on-line Resources for this article.
The first release of OSCAR was in April 2001, and since then we have released two major versions. Our release cycle usually coincides with the SuperComputing Conference, which is held annually in November. The current version as of writing is 3.0, and we are aiming at releasing 4.0 by SuperComputing04.
The goal of OSCAR is to provide users with the best practices for installing, programming and maintaining HPC clusters. Many open-source components individually work well in an HPC environment but require specific setup routines. OSCAR acts as the glue to integrate all these components together to provide a working toolkit. The project targets mid-size clusters (50+ node clusters). Community feedback suggests this size represents the majority of clusters assembled today.
OSCAR has the following components:
Administration: System Installation Suite (SIS), Cluster Command and Control (C3) and OPIUM (user management).
HPC tools: the parallel programming libraries: MPICH, LAM/MPI and PVM; batch systems: OpenPBS/MAUI, Torque and SGE; monitoring tools: Ganglia and Clumon; and other third-party OSCAR packages.
Core infrastructure/management: OSCAR Database (ODA) and OSCAR Package Downloader (OPD).
OSCAR developers are spread out over various geographical locations and weekly teleconferences are set up to discuss ongoing development issues. The group also holds annual general meetings to brainstorm new features to be included in future releases. An annual symposium also is held, usually in conjunction with HPCS (International Symposium on High Performance Computing Systems and Applications) where users are encouraged to present papers on their experiences with OSCAR as well as other development work relating to HPC. The 2nd annual OSCAR Symposium commenced in May 2004 in Winnipeg, Canada, and the proceedings are now available.
Bioinformatics is the marriage between biology and computer science/IT and is a rapidly growing field with high stakes in the HPC world. In simple terms, bioinformatics is concerned with the use of computer algorithms and systems to analyze biological data such as DNA, RNA, protein and regulatory elements.
Biological data are mainly string sequences. The analyses are usually string manipulations making Perl the programming language of choice for most bioinformaticians. Many open-source Perl programmers contribute to the Bioperl effort, which is a deposit of Perl modules specifically for performing bioinformatics analyses. Java is employed for larger projects and often for projects that involve graphical interfaces. Python is gaining a strong foothold in the field as more programmers learn about the ease of use and high readability of this relatively new but powerful programming language.
Linux clusters are very popular within the bioinformatics community, because a lot of the analyses tend to be long-running and repetitive. Linux clusters are ideal for running such embarrassingly parallel jobs that can be executed independently of one another. These are not considered true parallel programs because they do not need parallel programming libraries such as MPI. Bioinformatics routinely performed on clusters encompass running multiple scripts and algorithms on different inputs and can be executed on different CPUs, each with its own address space.
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