Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models
Abstract
The spatial scale, runtime speed, and behavioral detail of epidemic outbreak simulations altogether require the use of large-scale parallel processing. Here, an optimistic parallel discrete event execution of a reaction-diffusion simulation model is presented. Rollback support is achieved with the development of a novel reversible model that combines reverse computation with a small amount of incremental state saving. Parallel speedup and other runtime performance metrics of the system are tested on a small (8,192-core) Blue Gene / P system, while scalability is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes (up to several hundreds of millions in the largest case) are exercised.
Kalyan Perumalla
R&D Manager
Kalyan Perumalla is an R&D Manager with 25 years of experience. As a Federal Program Manager in Advanced Scientific Computing Research at the U.S. Dept. of Energy, Office of Science, Kalyan Perumalla manages a $100-million R&D portfolio covering AI, HPC, Quantum, SciDAC, and Basic Computer Science. He previously led advanced R&D as Distinguished Research Staff Member at the Oak Ridge National Laboratory (ORNL) developing scalable software and applications on the world’s largest supercomputers for 17 years, including as a line manager and a founding group leader. He has held senior faculty and adjunct appointments at UTK, GT, and UNL, and was an IAS Fellow at Durham University.