First-Principles Molecular Dynamics (FPMD) methods, although powerful, are notoriously expensive computationally due to the quantum modeling of electrons. Traditional FPMD approaches have typically been limited to a few thousand atoms at most, due to O(N3) or worse solver complexity and the large amount of communication required for highly parallel implementations. Attempts to lower the complexity have often introduced uncontrolled approximations or systematic errors. Using a robust new algorithm, we have developed an O(N) complexity solver for electronic structure problems with fully controllable numerical error. Its minimal use of global communications yields excellent scalability, allowing for very accurate FPMD simulations of more than a million atoms on over a million cores. At these scales, this approach provides multiple orders of magnitude speedup compared to the standard plane-wave approach typically used in condensed matter applications, without sacrificing accuracy. This will open up entire new classes of FPMD simulations, e.g. dilute aqueous solutions.

We have developed a highly efficient and scalable cardiac electrophysiology simulation capability that supports groundbreaking resolution and detail to elucidate the mechanisms of sudden cardiac death from arrhythmia. We can simulate thousands of heartbeats at a resolution of 0.1 mm, comparable to the size of cardiac cells, thereby enabling scientific inquiry not previously possible. Based on scaling results from the partially deployed Sequoia IBM Blue Gene/Q machine at Lawrence Livermore National Laboratory and planned optimizations, we estimate that by SC12 we will simulate 8 -- 10 heartbeats per minute -- a time-to-solution 400 -- 500 times faster than the state-of-the-art. Performance between 8 and 11 PFlop/s on the full 1,572,864 cores is anticipated, representing 40 -- 55 percent of peak. The power of the model is demonstrated by illuminating the subtle arrhythmogenic mechanisms of anti-arrhythmic drugs that paradoxically increase arrhythmias in some patient populations.

With supercomputers anticipated to expand from thousands to millions of cores, one of the challenges facing scientists is how to effectively utilize this ever-increasing number. We report here an approach that creates a heterogeneous decomposition by partitioning effort according to the scaling properties of the component algorithms. We demonstrate our strategy by developing a capability to model hot dense plasma. We have performed benchmark calculations ranging from millions to billions of charged particles, including a 2.8 billion particle simulation that achieved 259.9 TFlop/s (26% of peak performance) on the 294,912 cpu JUGENE computer at the Jülich Supercomputing Centre in Germany. With this unprecedented simulation capability we have begun an investigation of plasma fusion physics under conditions where both theory and experiment are lacking--in the strongly-coupled regime as the plasma begins to burn.