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ACMD Seminar: The "White Rat" of Numerical Reproducibility

Professor Michael Mascagni
Depts. of Computer Science, Mathematics, Scientific Computing, Florida State University
Applied and Computational Mathematics Division, NIST

Tuesday, June 19, 15:00 - 16:00
Building 227 Room A202

Tuesday, June 19, 13:00 - 14:00
Building 1 Room 4072

Abstract: We explore an application from the author's work in neuroscience.  A code used to investigate neural development modeled 100 neurons with all-to-all excitatory connectivity.  We used a simple ordinary differential equation system to model each neuron, and this model was used to produce a paper published in the Journal of Neurophysiology.  Later a colleague used our code to continue this work, and found he could not reproduce our results.  This lead us to thoroughly investigate this code and we discovered that it offered many different ways to thwart reproducibility.

Numerical reproducibility is considered a task that directly follows from the determinism in computations.  However, reproducibility has become an intense concern and and issue for research.  In fact, the author developed an international workshop of numerical reproducibility that is now a regular offering at the annual Supercomputing XX conference.  We will show how this particular code provides a lack of reproducibility from the following sources:

  • The non-associativity of floating-point operations in two ways
  • Differences in library mathematical functions whose reliability and correctness we take for granted

This code's sensitivity makes it a very powerful tool to explore many different manifestations of numerical reproducibility.  However, this code is by no means exceptional, as in neuroscience these types of models are used extensively to gain insights on the functioning of the nervous system.  In addition, these types of models are widely used in many other fields of study.

This is joint work with Prof. Wilfredo Blanco in CS at Universidade do Estado do Rio Grande do Norte in Brazil, and Woohyeong Kim, who is currently my graduate student.

Bio: 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).

Note: Visitors from outside NIST must contact Cathy Graham; (301) 975-3800; at least 24 hours in advance.


Created June 12, 2018, Updated June 2, 2021