Abstract: We will look at random number generation from the point-of-view of Monte Carlo computations. Thus, we will examine several serial methods of pseudorandom number generation and two different parallelization techniques. Among the techniques discussed with be 'parameterization,' which forms the basis for the Scalable Parallel Random Number Generators (SPRNG) library. SPRNG was developed several years ago by the author, and has become widely used within the international Monte Carlo community. SPRNG is briefly described, and the lecture ends with a short revue of quasirandom number generation. Quasirandom numbers offer many Monte Carlo applications the advantage of superior convergence rates.

Short Bio: Professor Michael Mascagni received his Ph.D. in Mathematics from NYU's Courant Institute of Mathematical Sciences where he worked in mathematical biology and the numerical solution of partial differential equations. He then moved to the Washington, DC area as a National Research Council Post-Doctoral Fellow at NIDDK's Mathematical Research Branch at the National Institutes of Health in Bethesda. Following, he accepted a staff position at the Institute for Defense Analyses's new Supercomputing Research Center (now the IDA Center for Computing Sciences) in Bowie, Maryland. At the beginning of 1997, he moved back to academia to coordinate the Doctoral Program in Scientific Computing at the University of Southern Mississippi. Recently, he accepted a new position in the Department of Computer Science at Florida State University. His research interests are Monte Carlo methods, numerical analysis, numerical solution of partial differential equations, parallel computing, random number generation, cryptography, distributed computing, and scalable numerical libraries.