The Facility
CGRB Server room

The CQLS Biocomputing and Bioinformatics facility provides wide-ranging resources, expertise and support for computational needs of the molecular biosciences community at Oregon State University. The facility offers a robust computing environment for high-level computational biology and a versatile intellectual resource for interdisciplinary collaboration.

The first Genome cluster was a compute farm in the Beowulf tradition (2001); i.e., built with commodity hardware and freely available software. Growing to include more than 60 CPU's, Genome served both as a test bed for CQLS developers and system administrators and as an increasingly powerful tool for OSU faculty, staff and students. Learning from setbacks and capitalizing on successes, Genome has steadily grown to include more than 6000 processors. The new version of the Genome is currently has none of the original commodity hardware left. Space and BTU constraints motivated the migration to rack-mount multi-processor nodes and the need to address large amounts of RAM instigated the move to 64-bit hardware. Right now, about 90% of Genome is composed of rack-mount multi-processor nodes that contain 4, 8, 16, 24, 32, 40, 48, 64, 80, 120, 160 and 256 thread systems from IBM, AMD or Intel based multi-core processors.

The Center for Quantitative Life Sciences maintains an extensive and well-managed infrastructure consisting of a distributed service architecture, a greater than 6000-processor computer cluster and a secure private 1G/10G/40G/100G network. Each machine has internal hard drive disk space, but is also connected over 9PB of NFS shared disk space. The CQLS encourages high-volume users to contribute to the computational infrastructure. Users are charged $32, $64, and $96 per/month for maintaining each processing machine, web/database server, and file server, respectively. The nodes are provided at the highest priority to the specific project and provided upon request at a lower priority to other CQLS researchers. Thus, subsets of cluster nodes are dedicated to specific research projects, but they function as part of a unified cluster when needed for intensive jobs. This priority-based scheduling has proven quite successful, both in terms of end-user satisfaction and in the execution of systems administration activities. Computational requirements are constantly re-evaluated and new hardware is integrated as needed. Researchers who do not own equipment and can not complete their work within the resources provided to general users can rent resources from the CQLS as needed to complete the work on time. Research rental hardware includes processing machines (including machines with 1TB of RAM), web/database servers and file space to store data.


Contact Biocomputing Staff