"Grand Challenges in Data-Intensive Discovery" conference will be held October 26 - 28, 2010 at the San Diego Supercomputer Center (SDSC) on the campus of UC San Diego.
Science has entered a data-intensive era, driven by a deluge of data being generated by digitally based instruments, sensor networks, and simulation devices. Hence, a growing part of the scientific enterprise is associated with analyzing such data, and such analysis places special demands on computer architectures because the associated calculations have frequent I/O accesses, large memory requirements, and often limited parallelism.
In mid 2011, SDSC will deploy a unique data-intensive high performance computing system called Gordon. Gordon will be a peer-reviewed allocated resource on the National Science Foundation's TeraGrid available to any US researcher. It will have a peak speed expected to be in excess of 200 Teraflops and feature very large shared memory nodes and 1/4 petabyte of flash SSD memory to vastly accelerate large database and data mining applications.
The goal of the GCDID conference is to provide an opportunity for attendees to share their expertise while exchanging ideas about the computational challenges and concerns common to data-intensive problems. Specifically, the conference is structured to facilitate discussion to help:
- Articulate and clarify "Grand Challenges" in data-intensive research across a broad range of disciplines, including arts, astronomy, biology, computer science, earth sciences, economics, engineering, humanities, medicine, neuroscience, social sciences, and data-related technologies
- Identify applications and disciplines that can benefit from Gordon's unique architecture and capabilities, including those that have not been part of the traditional HPC community
- Identify common technical needs across disciplines and relevant software solutions
- Recognize opportunities for leaders in data-intensive science to take advantage of SDSC's available expertise in this area
Topics to be discussed will include arts, astronomy, biology, computer science, earth sciences, economics, engineering, humanities, medicine, neuroscience, social sciences, and data-related technologies.
