2010 Best Practices Competition IT & Informatics HPC
IT Informatics - Cambridge Healthtech Institute
IT Informatics - Cambridge Healthtech Institute
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location and retention of key data. Specifically, <strong>IT</strong> took the following steps to improve the data<br />
management process and accelerate the scientific workflow:<br />
• Dedicated NFS storage for raw reads, attached to back-up tape library<br />
• Dedicated NFS storage for all results, attached to back-up tape library<br />
• Automated backup process for “key” files<br />
• User education on how to mount/unmount the storage space<br />
• Configured Aspera server to read directly from designated NFS mount points eliminating<br />
unnecessary data moves<br />
• Weekly cron jobs for monitoring and informing users about storage resource capacity<br />
• Automated monitoring of user jobs utilizing the <strong>HPC</strong> Cluster<br />
• Established a SharePoint based web portal to share NextGen project related information<br />
These changes had to be synchronized and communicated across multiple scientific divisions as well as<br />
the within the <strong>IT</strong> department. The end result was a more streamlined scientific workflow, improved data<br />
management environment and reduced impact on the storage, backup and network infrastructure.<br />
Lesson, be flexible in regards to data management procedures and the supporting infrastructure. Rapidly<br />
advancing technologies such as NextGen sequencing can render your current methods obsolete and you<br />
must be willing to make dramatic changes in response to the needs of the scientific community and the<br />
demands of the technology.<br />
Benchmarking:<br />
Alignment of billions of reads to reference genomes is computationally expensive. An effort was initiated<br />
to benchmark sequence alignment tools. TGen’s <strong>IT</strong> team was actively involved in this process by<br />
providing several performance measurement and tuning tools and creating automated scripts for<br />
collecting data about computing resource utilization associated with six popular sequence alignment<br />
programs. <strong>IT</strong> used performance measurement tools for cluster computing environments to benchmark<br />
the speed, CPU utilization and input-output bandwidth needed for the program. This information is now<br />
being used for selecting the best tool for various projects and planning the resource requirements for<br />
future NextGen sequencing projects. Lesson, time spent benchmarking can provide significant benefit in<br />
terms of reducing the cost and effort associated with the “trial & error” approach to selecting and using<br />
complex technology such sequencing alignment tools.<br />
[Results]<br />
Key Technologies & Supporting Methodologies<br />
The TGen High Performance Bio-Computing Center (HPBC) manages a diverse collection of <strong>HPC</strong><br />
systems, storage and networking resources, including two large supercomputers. The first supercomputer<br />
is called Saguaro2, and is a Dell Linux cluster. This system consists of ~4000 Intel x86-64 processor<br />
cores, with 2 GB RAM per core. This system has a shared parallel 250 TB (Lustre) file system that allows<br />
massive amounts of concurrent input/output operations spread across many compute nodes. This system<br />
is very effective at running thousands of concurrent discrete processing jobs, or at running very large<br />
parallel processing workloads. This large <strong>HPC</strong> cluster system is installed at the Arizona State University<br />
campus in the Fulton High Performance Computing Initiative (<strong>HPC</strong>I) center and was funded via NIH grant<br />
S10 RR25056-01.
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