Many parallel quantum Monte Carlo (QMC) applications rely on the use of an ensemble data structure that represents the quantum state of the simulation. Ensemble data is typically represented using a large spline interpolation table that becomes read-only after initial data has been loaded. Although only a small fraction of the table may be accessed by each thread or process at any given time, the accesses are random. Hence, current implementations of these methods typically use replicated copies of the entire interpolation table at each node of a parallel computer. This limits scalability since increasing the number of processors does not enable larger systems to be run.

In this talk, I will present an automated data management approach that enables existing QMC codes to be adapted with minimal changes to enhance the range of problem sizes that can be run. We utilize the Global Arrays partitioned global address space model to provide efficient distributed, shared storage and enhance the performance of our system through data layout optimization, caching, and replication.

Bio: Qingpeng Niu is starting his third year as a PhD student in the Department of Computer Science and Engineering of the Ohio State University, under the guidance of Dr. Sadayappan. He is interested in systems research for parallel computing. In the past, he got his bachelor and master degrees from Northeastern University, Shenyang, China.