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Life Sciences
Pharmaceutical companies, biotechnology firms, and research institutions are actively involved in developing and exploiting HPC simulations which produce massive datasets that provide better insight into human diseases. This explosion in genomic and proteomic information and emergence of data-intensive molecular diagnostic techniques demand a scalable, high performance storage infrastructure.

The Panasas ActivStor Parallel Storage Cluster provides scalable bandwidth and random I/O to accelerate throughput for the most demanding life sciences HPC applications. Scalable capacity ensures that multiple versions of genomic databases can be maintained and quickly made available to compute cluster nodes for computational chemistry, bioinformatics, and other compute and data-intensive HPC applications. Large datasets, such as micro array or mass spectrometry images, are effectively managed within a single scalable namespace and shared among researchers to streamline collaboration.

Panasas Life Science customers include:
  • Boehringer Ingelheim
  • The European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute (EBI)
  • The National Center for Biotechnology Information (NCBI)
    A division of the National Library of Medicine (NLM) at the National Institutes of Health (NIH)
  • Novartis
  • The Rockefeller University - Gaasterland Laboratory of Computational Genomics
  • University of California: Berkeley - Center for Integrative Genomics
  • University of California: Santa Cruz - Center for Biomolecular Sciences and Engineering
  • University of California: San Francisco - Sali Laboratory

 Resources
Rockefeller Success Story
Stanford University
UC Berkeley Success Story
UC San Francisco Success Story
UC Santa Cruz Success Story
Solutions Note
New Styles in Storage Architecture
"The system is blazing fast, we've been able to eliminate our I/O bottleneck so researchers can analyze data more quickly…. The product is 'plug-and-play' at all levels. Our team doesn't have to think about it at all. We can just do our work."
Dr. Terry Gaasterland, Associate Professor of The Rockefeller University
Gaasterland Laboratory of Computational Genomics


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