SC17 Denver, CO

Impacting Cancer with HPC: Opportunities and Challenges

Authors: Dr. Eric Stahlberg (Frederick National Laboratory)

Abstract: High-performance computing has long been employed in cancer research and clinical applications. Ongoing challenges of deriving insight from increasing amounts of cancer related information have accelerated the significance of HPC and collaborations in cancer research, in areas including simulations, data analytics and deep learning. Underscored by several recent high-profile efforts, communities are rapidly developing where HPC is applied to cancer challenges. This SC17 BoF provides an important venue, bringing these communities together around a common theme of HPC and cancer. Following the successful format of past years, the BoF will be informative and highly-interactive with presentations and significant attendee discussion.

Long Description: Cancer is a disease that touches us all. As the importance of HPC and HPC technologies has grown in medical applications, underscored by several recent large scale initiatives including the 21st Century Cures Act, the Cancer Moonshot, the Precision Medicine Initiative, Cancer Systems Biology Consortium and may efforts employing machine and deep learning involving large scale cancer data and high-performance computing. it is important to bring together the multiple interests as a community to increasingly share insight and expertise with a specific focus on cancer. The session aims to bring together those in the HPC field who have an interest in understanding and expanding the impact that HPC can have on cancer.

The BoF is being organized to provide a framework for extending the learning and awareness about the significant role HPC has in fighting and treating cancer, both in specific domains and across many available technologies. The need to convene across technology domains is essential since both simulation and big data are key to making progress on this disease. Multiple HPC technologies used significantly in cancer prevention, diagnosis, treatment, monitoring and validation. The need for HPC in cancer precision medicine has become central to efforts in predictive oncology, data sharing, more rapid treatment identification, and rapid diagnosis. Several announcements in 2016 and 2017 underscore the current significant role for HPC with utilization expected to grow substantially in the future as the ability to gather, integrate and analyze data join with the simulation capabilities of tomorrow’s exascale systems.

The first two HPC and Cancer BoFs have both been well attended by participants across the HPC and scientific fields with an interest in building collaborations, gaining insight into shared challenges, and seeking new opportunities to contribute to advances in overcoming cancer. When asked at SC16, the attendees enthusiastically responded to having the session again at SC17.

Historically represented areas in the BoF session included: HPC machine learning, bioinformatics, parallel programming, big data, computer systems architecture, FPGAs, physics, biomedicine, chemical engineering, virology, GPU computing, computational physics, pattern recognition, software development, aerospace/CFD, crystallography, mathematics, storage, computer science, structural biology, IT infrastructure, protein structure, visualization, systems biology

Background and specific examples about the use of HPC technology in cancer applications, will provide the common basis for discussion in the BoF. To provide a rapid overview of the broad domain, presenters will provide insight into the various ways specific technologies are being used in cancer applications, while also presenting across a range of technologies used in specific cancer areas.

Discussions among attendees will be fostered initially on topics identified in the presentations, and expanding to topics of attendee interest. Time is reserved for individuals to meet and discuss individually following presentations and ensuing audience interactive discussions.

Key challenges and opportunities will be shared with all attendees.

Conference Presentation: pdf

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