Monte Carlo and other simulation methods are a class of computations that have always been unusually suitable to parallel computation. However, the realization of efficient parallel simulation depends of the quality of the random number generation tools available. This is especially true with parallel random number generation, where issues arise in testing the quality of a group of random number streams when they are used simultaneously. Work on these problems produced the Scalable Parallel Random Number Generators (SPRNG) library. This is a very popular library that was widely adopted in the Monte Carlo community on distributed-memory high-performance computing (HPC) systems.  Current HPC systems are incorporating multicore, GPU-based accelerators, and the Intel Phi to achieve ever higher performance within ever more strict power constraints.  In this talk we will discuss these architectural developments in HPC, especially at the exascale, and what this requires of the next generation of random number generation tools. We then describe how SPRNG is being upgraded to meet these more stringent requirements. Of particular emphasis with be the nonlinear, multiplicative lagged-Fibonacci generator, that has many desirable properties, has been available in SPRNG for many years, and show great promise as a simple generator family that can provide for many of the modern random number requirements for simulation on HPC systems, even at the Exascale.
This is joint work with Drs. Yue Qiu of FSU and Timothy Anderson of Daniel H. Wagner, Associates.

Michael Mascagni is an internationally recognized expert on all aspects of random number generation and Monte Carlo methods, and has lectured extensively across the globeon theses and related topics. He received his undergraduate degrees in Biomedical Engineering and Mathematics at the University of Iowa in 1981, and entered Rockefeller University to study neurobiology. While taking some math courses at NYU he decided to switch to math, and he moved to the Courant Institute in 1983. He graduated in 1987, having worked with Prof. Charlie Peskin on the numerical solution of nerve equations. He has published over 100 scholarly articles, has graduated doctoral students in Computer Science, Mathematics, and Scientific Computing, and he currently leads a research group working in high-performance computing aspects of Monte Carlo methods and random number generation. He is an editor for many journals including Monte Carlo Methods and Applications, Mathematics and Computers in Simulation, and the ACM Transactions on Mathematical Software. He has been a visiting faculty member at Université de Toulon et du Var, Universität Salzburg, Universität Kaiserslautern, Università degli Studi di Padova, and the King Abdullah University of Science and Technology. He also spent a sabbatical year visiting the Seminar für Angewandte Mathematik, Departement Mathematik, Eidgenössische Technische Hochschule (ETH-Zürich). He was elected an Association for Computing Machinery (ACM) Distinguished Scientist in 2011, and is currently a Faculty Appointee at the National Institute of Standards and Technology (NIST).