Name: Prof. Michael Mascagni
Address: Department of Computer Science and
Department of Mathematics and
Department of Scientific Computing and
Graduate Program in Molecular Biophysics
Florida State University
Tallahassee, FL 32306-4530 USA
AND
Information Technology Laboratory
Applied and Computational Mathematics Division
100 Bureau Drive M/S 8910
National Institute of Standards and Technology (NIST)
Gaithersburg, MD 20899-8910 USA
Offices: 498 Dirac Science Library/207A Love Building (FSU) Building 225/Room B154 (NIST)
Phone: +1.850.644.3290 (FSU) +1.301.975.2051 (NIST)
FAX: +1.850.644.0058
e-mail: mascagni@fsu.edu (FSU) mascagni@nist.gov (NIST)
Title: Random Number Generation Tools for Distributed Simulation on Modern HPC Architectures
Abstract:
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.
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