SC|06 Powerful Beyond Imagination
SC06 is the International Conference for High Performance Computing Networking and Storage

About Registration Conference Technical Program Exhibits News and Press Travel

Home Conference HPC Competitions HPC Analytics Challenge






HPC Analytics Challenge

Submissions deadlines for the 2006 HPC Analytics Challenge are past.
Join us to hear our 2006 HPC Analytics Challenge Finalists present their work: Tuesday, Nov. 14 at 1:30 p.m. in room 24-25.

Watch for information on the 2007 challenge soon.

Questions


HPC Analytics: Transforming Data into Insight
More than ever before, organizations in commercial, government, university and research sectors are tasked with making sense of huge amounts of data. These dynamics have led to the growing area of HPC analytics, and SC06's HPC Analytics Challenge highlights three powerful examples.

We define high performance analytics as the technology that allows sophisticated analysis of phenomena, data or information. Analytics applies integrated computational methods on massive amounts of data to support critical thinking and reasoning, leading to new insights and understanding in the context of applied challenges.

The primary goal of the challenge is to provide a forum for researchers, engineers, and analysts to showcase computationally intensive applications that solve real-world, complex problems through the use of rigorous and sophisticated methods of data analysis and high end visualization in conjunction with the use of high performance computing, networking, and storage.

Click here to make submissions

Questions

Intel Logo

THANK YOU to Intel Corp. for their generosity in funding the HPC Analytics Challenge at SC06.

Introducing the 2006 Finalists for the HPC Analytics Challenge
Three teams, all responsive to our definition of high performance analytics, have been judged by a panel of nine experts and recognized as Finalists. They will be presenting their work at the Analytics Challenge session help on Tuesday, November 14, at 1:30pm in room 24-25. At the end of the session, the SC06 Analytics Challenge winning submission will be recognized.

This will be an exciting completion of the 2006 Analytics Challenge! The efforts are both remarkable and fascinating considering diversity, content, and import. Please join us Tuesday afternoon and see and hear what the finalists have done.

"Remote Runtime Steering of Integrated Terascale Simulation and Visualization," Hongfeng Yu _ Tiankai Tu, Jacobo Bielak, Omar Ghattas, Kwan-Liu Ma, David R. O'Hallaron, Nathan Stone, Ricardo Taborda-Rios, John Urbanic Carnegie Mellon University, University of California, Davis, and The University of Texas at Austin, Pittsburgh Supercomputing Center

We have developed a novel analytic capability for scientists and engineers to obtain insight from ongoing large-scale parallel unstructured mesh simulations running on thousands of processors. The breakthrough is made possible by a new approach that visualizes partial differential equation (PDE) solution data simultaneously while a parallel PDE solver executes. The solution field is pipelined directly to volume rendering, which is computed in parallel using the same processors that solve the PDE equations. Because our approach avoids the bottlenecks associated with transferring and storing large volumes of output data, it offers a promising approach to overcoming the challenges of visualization of petascale simulations. The submitted video demonstrates real-time on-the-fly monitoring, interpreting, and steering from a remote laptop computer of a 1024-processor simulation of the 1994 Northridge earthquake in Southern California.

"Computational Oral and Speech Science on E-science Infrastructures," Masaaki Noro, Kazunori Nozaki, Masashi Nakagawa, Susumu Date, Kenichi Baba, Steven Peltier, Hiroo Tamagawa, Toyokazu Akiyama, Shinji Shimojo Osaka University, Japan, National Institute of Information and Communication Technology, Japan, NCMIR University of California, San Diego, Osaka University Dental Hospital, Japan

We demonstrate an oral scientific simulation and its visualization based on E-science. This oral scientific application will become an essential key component for medical and dental clinic in the near future because Bio-Medical simulations will provide a clinical index considering a prognostic of a disease. In this case, it was shown that the physical theory of sound production of speech sound, sibilant. However, it is difficult to acquire the computational and storage resources in the hospitals. Our E-science infrastructure enables scientists and clinicians to achieve the advanced information produced by simulations. As the result of this phase implementation for Bio-Medical simulation on E-science infrastructure, we could extract the scientific findings about the oral science. Moreover, this infrastructure can be used more generally because of the divided architecture between applications and E-science infrastructure.

"High-throughput visual analytics for biological sciences: turning data into knowledge," Christopher Oehmen, Lee Ann McCue, Joshua N. Adkins, Katrina Waters, Tim Carlson, William Cannon, Bobbie-Jo Webb-Robertson, Douglas Baxter, Elena Peterson, Mudita Singhal, Anuj Shah, Kyle Klicker Pacific Northwest National Laboratory, Richland, Washington

For the SC|06 analytics challenge, we demonstrate an end-to-end solution for processing data produced by high-throughput mass spectrometry (MS)-based proteomics so biological hypotheses can be explored. This approach is based on a tool called the Bioinformatics Resource Manager (BRM) which will interact with high-performance architecture and experimental data sources to provide high-throughput analytics to a specific experimental dataset. Peptide identification was achieved by a high-performance code, Polygraph, which has been shown to scale well beyond 1000 processors. Visual analytics applications such as PQuad, Cytoscape, or others may be used to visualize protein identities in the context of pathways using data from public repositories such as Kyoto Encyclopedia of Genes and Genomes (KEGG). The end result was that a user can go from experimental spectra to pathway data in a single workflow reducing time-to-solution for analyzing biological data from weeks to minutes.




Home | About | Contact Us | Registration | Sitemap
IEEEComputer SocietyACM