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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20151118T170000Z
DTEND:20151118T173000Z
LOCATION:18AB
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: As personalized medicine becomes more integrated into healthcare, the rate at which humans are being sequenced is rising quickly with a concomitant acceleration in compute and data requirements. To achieve the most effective solution for genomic workloads without re-architecting the industry-standard software, we performed a rigorous analysis of usage statistics, benchmarks and available technologies to design a system for maximum throughput. We share our experiences designing a system optimized for Genome Analysis ToolKit pipelines, based on an evaluation of compute, workload and I/O characteristics. The characteristics of genomic-based workloads are vastly different than traditional HPC workloads requiring radically different configurations of the scheduler and I/O to achieve scalability. By understanding how our researchers and clinicians work, we were able to employ new techniques to not only speed their workflow yielding improved and repeatable performance, but we were able to make efficient use of storage and compute nodes.
SUMMARY:Big Omics Data Experience
PRIORITY:3
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