2019 Computer Science Colloquium Series

This is a tentative schedule and more information will be posted as soon as possible. Details are subject to change.

Wednesday, August 21: David Poliakoff (Sandia National Lab)
Wednesday, August 28: Aidan Thompson (Sandia National Lab)
Wednesday, September 4: Brandon Schmandt (UNM)
Tuesday, September 10: David Doty (UC Davis) *Please note the date change from Wednesday to Tuesday*
Wednesday, September 18: Imani Palmer
Wednesday, September 25: Peter Hraber (Los Alamos National Lab)
Wednesday, October 2: Yan Guo (UNM HSC)
Wednesday, October 9: No Class (Day Before Fall Break)
Wednesday, October 16: Lisa Kuuttila (UNM STC)
Wednesday, October 23: William Byrd (University of Alabama at Birmingham)
Wednesday, October 30: Ramyaa (NM Tech)
Wednesday, November 6: Eirini Eleni Tsiropoulou (UNM ECE)
Wednesday, November 13: Jonathan Whetzel (Sandia National Labs)
Wednesday, November 20: Yi He (UNM)
Wednesday, November 27: No Class (Day Before Thanksgiving)
Wednesday, December 4: Alexander Koglin (NTxBio)

Compiler Tools Supporting Million Line Codes

David Poliakoff, Sandia National Lab

Wednesday, August 21, 2019
Centennial Engineering Center 1041
2:00-3:00 PM


The DOE National Labs are trying to ready million line codes written in the 70s for the supercomputers of the 2020s. This is proving to be a monumental effort, but one which can be helped immensely by compiler tools. In this conversation we'll discuss allowing these codes to set their own standards for how the code should be written and what it should do when it runs, and how compilers can be used to enforce those standards. To do so, we'll talk about the fundamental technologies we're using to target future supercomputers, the structure of the teams who work on these codes, and how to develop and promote tools to support their efforts. A mild sales pitch to apply for internships might be included.


David Poliakoff thinks about compilers, performance tools, and how to use them to answer questions about complex software. Currently he does this for Sandia National Laboratory, but has also done so for Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, and Louisiana State University. He learned to do this at Millsaps College and the University of Oregon.

Molecular Dynamics Simulation: Engine of Discovery or Bridge to Nowhere?

Aidan Thompson, Sandia National Lab

Wednesday, August 28, 2019
Centennial Engineering Center 1041
2:00-3:00 PM


Molecular dynamics (MD) is a powerful atomistic simulation tool for exploring the microscopic behavior of a wide variety of condensed matter systems by tracking the motion of large collections of atoms over millions of timesteps. It can be viewed as a bridge connecting macroscopic models based on continuum mechanics to rigorous quantum electronic structure calculations. Continuum mechanics simulations can describe complex phenomena and behaviors using macroscopic material models for things like viscosity, strength, and rates of reaction. They do not have the ability to explain the atomic-scale origins of these phenomena or to make predictions about new materials or physical conditions. Quantum methods can make very accurate predictions, but only for collections of atoms (100's) and spans of time (picoseconds) that are too small to be broadly useful. In MD, we simply replace the quantum mechanics with a classical interatomic potential (IAP) that expresses the total energy of the quantum system as a sum of contributions from atoms, bonds, etc. This greatly reduces the computational cost of classical MD relative to quantum methods. It also scales efficiently on parallel computers, so that large MD simulations can be distributed over thousands or millions of processors. This enables unique opportunities to validate and improve continuum models by direct comparison with atomistic simulation. However, the accuracy of these simulations is still limited by the quality of the IAP. This has lead to the development of increasingly more complex and computationally expensive IAPs that provide accurate descriptions of real materials. Moreover, because of the vast difference between the atomic and macroscopic scales there is an insatiable demand for bigger and longer simulations. So MD is constantly being pushed in three directions at once: accuracy, length, and time. In this talk, I will provide an overview of the molecular dynamics method, highlighting some recent developments in Sandia's LAMMPS code. I will then describe ongoing work in two areas (i) large-scale molecular dynamics simulation of chemical energy release in explosive crystals under shock loading (ReaxFF) (ii) automated generation of high-accuracy machine-learning IAPs using very large data sets of quantum calculations (SNAP). We have applied SNAP to a variety of complex material systems, including plasticity in tantalum, plasma-surface interactions in tungsten/beryllium bimetallic materials, and radiation damage in III-V semiconductors.


Dr. Thompson earned his undergraduate degree in Chemical Engineering at University College, Dublin, Ireland. He earned his Ph.D in Chemical Engineering at the University of Pennsylvania, 1994 in the area of statistical thermodynamics of complex fluids. Since 1997 he has worked in the Center for Computing Research at Sandia National Laboratories, first as a post-doctoral appointee, and since 2002 as principal member of the technical staff. Throughout that time he has worked as one of the core developers of the LAMMPS molecular dynamics code, while at the same time using it to study the atomic-to-mesoscale behavior of a wide variety of materials, described in over 50 publications. In recent years, frustrated with the limited accuracy of classical potentials, he has become a leading developer of machine-learned interatomic potentials fit to large databases of quantum calculations.