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The High-Performance Computing Program Development effort aims to foster the expanded use of a high-performance computing ecosystem to accelerate advances in predictive oncology research and clinical applications. Both driven and enabled by the rapid growth rates of information collected and generated about cancer, the opportunity for ever increasing computational capability grows as the data is analyzed, explored and utilized to provide critical insight into cancer. The program aims to develop the computational and data science ecosystem by addressing critical needs in compute, data transfer, data management, exploration and education in these areas required to advance the mission of the NCI.

*For support inquiries, please contact us at nci-cbiit-hpc@list.nih.gov

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Recent Updates

8/8/16

·  Frontiers of Predictive Oncology and Computing Meeting - With over 100 attendees from across the Department of Energy, NCI, academia, industry and other government agencies, the meeting (hosted by Intel July 12-14, 2016) provided an opportunity to gain insight into challenges and opportunities for the future. A white paper summarizing the meeting is to be developed.

·  New Data Services with Cleversafe – The Cleversafe storage system officially was moved into a production operational status at the beginning of August. Led by the IT Operations Group at Frederick National Laboratory and working with many stakeholders including CCR, CBIIT and NIH CIT, the new system is used within industry and in key efforts such as the Genomic Data Commons to provide a high level of data assurance for archive and stable data. Stay tuned for further information on opportunities to learn more how this new resource may benefit your scientific and operational needs.

·  Education and Training - Plans are underway to develop educational opportunities to learn more about how high-performance computing (HPC) can be used to accelerate cancer research and clinical applications. Individuals and groups interested in learning more about HPC, either in general or with specific technologies and scientific challenges in mind may reach out and contact Eric Stahlberg, Miles Kimbrough or George Zaki.

·  Computational Approaches for Cancer workshop - Scheduled for November 13, 2016 as part of the International Conference for High Performance Computing, Networking, Storage and Analysis. A call for papers has been issued. More information can be obtained at the link http://www.scworkshops.net/cancer2016/

CBIIT HPC Strategy

With a focus on providing robust and reliable solutions enabling NCI investigators to utilize HPC in their efforts, the CBIIT HPC strategy is focused on these interconnected areas:

• Working closely with investigators across NCI to enable broader utilization of HPC through HPC training, education and reliable system access
• Guided by investigator challenges and opportunities, provide support and consulting for HPC needs as well as development, optimization and/or validation of HPC applications useful to cancer research and clinical applications
• Effective data management and information delivery solutions in support of HPC applications used in cancer research
• Exploration and evaluation of emerging HPC technologies for use in cancer research, information delivery and data center operations
• Developing essential partnerships within NCI, NIH, HHS, government, academically, commercially, nationally and internationally fostering the expanded use of HPC in cancer research
• Develop and deliver services supporting current and future HPC needs of NCI investigators
• Continual incorporation of investigator input to improve and evolve HPC services, capabilities and opportunities

Long Range Guiding Objectives for HPC in Cancer Research

With guidance and insight provided by the cancer research and clinical community within NCI, deliver robust, reliable HPC capabilities and support that:

• Enable broader understanding of cancer, cancer system dynamics and cancer characterizations
• Enable rapid identification of potential cancer risks and presence of cancer in individuals
• Enable rapid determination of optimal treatment options for patients
• Expand treatment options through improved discovery and rapid, reliable validation
• Foster computational integration and cooperation across the global cancer research community
• Enable transfer and flow of HPC technologies between NCI and other stakeholders
• Enable NCI to take full advantage of computational advances to accelerate cancer research

Foundations for Successful HPC in Cancer Research and Clinical Development