HPCwire: Maintaining Nuclear Weapons in the Age of Supercomputers

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June 23, 2006

Maintaining Nuclear Weapons in the Age of Supercomputers

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Since 1992 there has been a moratorium on nuclear testing in the United States. This has forced the National Nuclear Security Administration (NNSA) to develop a program to maintain our nuclear stockpile by other means. The Advanced Simulation and Computing Program (formerly known as the Accelerated Strategic Computing Initiative, or ASCI) was established to create this capability. Under ASC, computer simulation capabilities are developed to analyze and predict the performance, safety, and reliability of nuclear weapons and to certify their functionality.

HPCwire got the opportunity to speak with the director of ACS, Dimitri Kusnezov, about the nature of the program and the significance of the IBM Blue Gene/L (BG/L) supercomputing technology in maintaining our nuclear stockpile.

HPCwire: Can you help us understand the big picture for NNSA's ASC program?

Kusnezov: Sure, it is actually a rather simple concept. If you don't want to test nuclear weapons like we did in the past on a weekly or monthly basis, but you still want to maintain our country's nuclear competence, you must develop the means to do it virtually. That in a nutshell is the ASC program. We push the limits of computational science and supercomputing to meet our evermore challenging national nuclear security needs.

We do so much virtually today, that computer simulation has become second nature -- similar to email's importance to how you and I work today. Today, NNSA uses the Stockpile Stewardship program to ensure the safety, security and reliability of the nation's nuclear deterrent without testing. It pulls together the theoretical models, experimental results, and legacy databases into an aggregated picture that weapon scientists use to assess and gain confidence in the viability of the nation's nuclear stockpile or how to deal with other emerging needs. Without this simulation capability, the country would have to consider renewed testing, which is something we don't want to do.

The world has changed considerably since the end of the Cold War. Our country adopted the nuclear test ban and then developed the ASC program to meet the needs through simulation. However, after a decade of this, we are now starting to transform NNSA's national enterprise of plants and labs to align with our new view of the future. As the Deputy Administrator for Defense Programs Tom D'Agostino recently stated, NNSA is moving towards a leaner, lower cost, more responsive posture that can meet changing requirements coming from the President and Congress. This complex will be called upon to rapidly certify both tested and possibly untested weapons in the future and to be prepared to meet unanticipated threats. These requirements place huge demands on predictive computing.

The second decade of ASC, and beyond, is therefore focused on predictive capability, which is fundamentally about using simulations to help quantify uncertainties that are sufficiently small to be useful in certification. Predictive computing will generate a very large demand for capacity at the capability level.

HPCwire: Why did you pursue BG/L and how is it different than other ASC platforms?

Kusnezov: We really hit a home-run with BG/L. But looking back at how we got here, it was a pretty tough road. There was remarkable skepticism and resistance to this system from both inside the laboratory complex and in the supercomputing community at large. What our labs have been able to do with it now though is beyond compare. Running, debugging and visualizing on more than 100,000 processors is now almost common practice. Today, if you look at the Top500 list, you see how broadly and globally this system is accepted which is a remarkable achievement. The strong support from the Congress and the Department of Energy has been paramount to our success.

Weapons simulations are perhaps the most complex simulations that currently exist. They require a tremendous collection of physics, chemistry, engineering and material science to get a single prediction. Given our need to better understand underlying science and to quantify our level of uncertainty, we recognized about six years ago that new classes of computers were necessary to meet the coming requirement for predictive simulation. We recognized as well that power consumption concerns, the cost of computer room floor space and the slower growth in microprocessor clock frequency improvements would otherwise overwhelm the program's ability to field the necessary computers.

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