Pearce, Olga

Educational Background

Ph.D., Computer Science, Texas A&M University, 2014
B.S., Computer Science and Mathematics, Western Oregon University, 2004
A.A., Business Administration, International Christian University, Kyiv, Ukraine, 2000

- Distributed Data Structures
- Parallel Algorithms
- Parallel Programming Models
- Performance Analysis, Optimization and Modeling
- Application Load Balancing

Weapons Simulation Codes Bronze Star Award, Lawrence Livermore National Laboratory, for developing GPU capabilities of programmatic application codes on Sierra, 2021
Weapons and Complex Integration Gold Award, Lawrence Livermore National Laboratory, for porting and optimization of programmatic application codes on Sierra, 2019
Weapons Simulation Codes Computational Physics Silver Star Award, Lawrence Livermore National Laboratory, for developing and applying the RAJA performance portability programming model, 2018
Deputy Director’s for Science and Technology Excellence in Publication Award, Lawrence Livermore National Laboratory, for work in application load balancing, 2015
Lawrence Graduate Scholar Program, Lawrence Livermore National Laboratory, 2009
National Science Foundation Graduate Research Fellowship, Texas A&M University, 2006

Bengisu Elis, Dai Yang, Olga Pearce, Kathryn Mohror, and Martin Schulz. QMPI: A Next Generation MPI Profiling Interface for Modern HPC Platforms. In Journal of Parallel Computing, 2020.
Christopher Zimmer, Scott Atchley, Ramesh Pankajakshan, Brian E. Smith, Ian Karlin, Matt Leininger, Adam Bertsch, Brian S. Ryujin, Jason Burmark, André Walker-Loud, M. A. Clark, and Olga Pearce. An Evaluation of the CORAL Interconnects. In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’19), 2019.
Olga Pearce, Hadia Ahmed, Rasmus W. Larsen, Peter Pirkelbauer, and David F. Richards. Exploring Dynamic Load Imbalance Solutions with the CoMD Proxy Application. In Future Generation Computer Systems (FGCS), 2018.
David Beckingsale, Olga Pearce, Ignacio Laguna, and Todd Gamblin. Apollo: Reusable Models for Fast, Dynamic Tuning of Data-Dependent Code. In IEEE International Parallel & Distributed Processing Symposium (IPDPS’17), Orlando, Florida, USA, May 29 - June 2, 2017.
David Boehme, Todd Gamblin, David Beckingsale, Peer-Timo Bremer, Alfredo Giménez, Matthew LeGendre, Olga Pearce, and Martin Schulz. Caliper: Performance Introspection for HPC Software Stacks. In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’16), Salt Lake City, Utah, USA, November 13-18, 2016.