Since 1987 - Covering the Fastest Computers in the World and the People Who Run Them

September 6, 2013

Iowa State Accelerates Science with GPU-Phi Supercomputer

Tiffany Trader

This summer Iowa State University took delivery of its most powerful supercomputer yet. “Cyence,” at a cost of $2.6 million, is capable of making 183.043 trillion calculations per second and has a total memory of 38.4 trillion bytes. To put that in perspective, one second of calculations by Cyence would take a single human five to six million years to complete, while the entire population of earth could perform the same calculation in 12 hours.

“Cyence” installed at Iowa State University. Photo by Bob Elbert.

Cyence has a rather unique configuration. The 4,768 core QDR InfiniBand cluster is comprised of 16-core SMPs and accelerated by GPUs and Phis. The bulk of the system employs 248 SuperMicro servers each with 16 cores, 128 GB of memory, Gigabit Ethernet and QDR (40Gb) InfiniBand interconnects. Two additional sets of 24 nodes are similarly outfitted, with the notable addition of NVIDIA K20 Kepler GPUs in one instance and Intel Phi Accelerator cards in the second. A large memory node contains 32 cores and 1 TB of main memory. The system runs Red Hat Enterprise Linux 6.4 and uses TORQUE (PBS) for resource and job management.

Like other computers of this league, Cyence is being used to design and generate models to solve challenging problems. Operational since early July, the machine has already begun to produce data for 17 research projects from eight Iowa State departments in a broad range of disciplines, including bioscience, ecology, fluid dynamics, earth and atmospheric science, materials science, and energy systems.

“The larger amount of computing power gives you better performance and makes the models you are using more realistic,” said Jim Davis, Iowa State’s vice provost for information technology and chief information officer.

The more powerful machine also enables a faster pace of research. The parameters of a model can be changed with greater ease and multiple results can be generated quicker, plus it allows allows multiple research groups to run models on the computer at the same time instead of just one group.

“This is very important to the research enterprise to have [Cyence] to carry out large scale research models,” Davis said. “This really shortens the time to discovery.”

Cyence is a source of pride for the entire campus. Installed at Iowa State in June, the 183-teraflop (peak) system barely missed the TOP500 mark, but that fact does not detract from its value to its new users, the majority of whom are ISU researchers and graduate students.

“It will make an impact on science,” proclaimed Davis. “We are providing facilities that faculty can use to accelerate their research work.”

Arun Somani, associate dean for electrical and computer engineering, led the team that applied for the National Science Foundation (NSF) grant in 2011. Recognizing the benefits of their proposal, the NSF allocated more than $1.8 million for the HPC system and Iowa State came up with a matching grant of $800,000. On the university side, the investment was shared among the colleges of Engineering, Liberal Arts and Sciences, and Agriculture and Life Sciences, and vice president of research and economic development office.

“This was a joint venture between the three colleges, which was very unique because you do not see this type of partnership at most universities,” said Somani, remarking on the project’s collaborative appeal.

Chief Information Officer Davis agreed: “The work leading up to the award has been a productive partnership of faculty from many disciplines working together and with university administration and information technology specialists.”

The university is already planning for its next HPC system, a so-called “condo cluster,” to be deployed next summer. The shared multi-departmental machine will further cement the collaborative element of HPC at Iowa State.

“The idea behind this is that faculty develop common requirements, pool their funds and buy a much larger system than they could individually,” Davis explained. “Costs are kept low by sharing support and infrastructure, and by pooling unused capacity from all stakeholders. Researchers can run jobs and simulations that are much larger than they would be able to otherwise.”

Related Items

Swiss Research House Outfits Cray XC30 with GPU Boosters

Supermicro Announces Support for New Intel Xeon Phi x100 Product Family 

Supermicro to Exhibit Supercomputing Solutions at NVIDIA GPU Technology Conference Japan

Material Science, Biomolecular Modeling Codes Add GPU Support

Share This