December 07, 2011
ITHACA, N.Y., Dec. 7 – The Cornell Center for Advanced Computing (CAC) received an HPC Innovation Excellence Award from the International Data Corporation (IDC) at the Seattle Hilton during SC11, the International Conference for High Performance Computing, Networking, Storage, and Analysis. The award was for enabling hepatitis C virus (HCV) research on a remote experimental MATLAB computing resource located at Cornell University.
Researchers from the Centers for Disease Control (CDC) used the Cornell resource to generate faster computations (more than 175 times speed-up), which provided a better understanding of networks of coordinated amino-acid variation that may enable the discovery of new therapeutic targets for the hepatitis C virus.
“IDC research has shown that high performance computing can greatly improve ROI and scientific advancement,” said Earl Joseph, program vice president, High Performance Computing at IDC.
With the cost per liver transplantation in the range of $280,000 for one year, liver transplantation for hepatitis C alone reaches a total cost of nearly $300 million per year. Moreover, the average lifetime cost for hepatitis C, in the absence of liver transplant, has been estimated to be about $100,000 for individual patients. Assuming that 80% of the 4.5 million Americans believed to be infected develop chronic liver disease, the total lifetime cost for this group (3.6 million) will be a staggering $360 billion in today's dollars. Assuming an estimated survival of 40 years, the annual health care costs for the affected U.S. population with chronic hepatitis C may be as high as $9 billion.
"The Council on Competitiveness would like to congratulate all the winners of the HPC Innovation Excellence Award and thank all of those who submitted entries. The significance of HPC to the private sector will only be fully appreciated when examples such as these are recognized for their economic value," said Dr. Cynthia McIntyre, senior vice president for the HPC Initiative at the Council on Competitiveness.
Cornell CAC systems and consulting staff configured the MATLAB computing cluster and wrote parallel MATLAB client code to provide transparent user access. CDC researchers were then able to prototype their application on their desktops using MathWorks’ Parallel Computing Toolbox and seamlessly scale up to the Cornell cluster using MATLAB Distributed Computing Server.
“Research on HCV is just one of the many projects that kept the MATLAB experimental resource saturated,” explained David Lifka, principal investigator of the National Science Foundation-funded project that received additional support from Dell, Intel, Microsoft, and MathWorks. “Over 500,000 jobs ran on the system in two years, generating new scientific insights and publications in condensed matter physics, gravitational wave detection, biomedical imaging, orthopedics, neuroscience, and optics,” he noted. In addition, project partner Purdue University enabled the resource to work as a transparent and efficient computational engine for nanotechnology applications that researchers from across the nation could easily access through the nanoHUB.org Science Gateway.
Using lessons learned from the project, Cornell is now investigating the use of cloud computing as a flexible platform for delivering certain types of HPC resources and software. Launched last month, Cornell’s Red Cloud initiative includes an Infrastructure as a Service (IaaS) platform with virtual servers and virtual disks and as well as a Software as a Service (SaaS) that features MATLAB Distributed Computing Server with NVIDIA GPUs for enhanced performance.
For more information on the IDC HPC Innovation Excellence Award, visit https://www.hpcuserforum.com/innovationaward/.
Award sponsors include Adaptive Computing, Altair, AMD, Ansys, Appro, Avetec/DICE, the Boeing Company, the Council on Competitiveness, Department of Defense, Department of Energy, Ford Motor Company, Hewlett Packard, IBM, HPCwire, insideHPC, Intel, Microsoft, National Science Foundation, NCSA, Platform Computing, Scientific Computing, and SGI.
Source: Cornell University
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