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mathematical sciences, Computational Sciences & Mathematics

With multidisciplinary expertise spanning technical pillars of high-performance computing, data science, and computational mathematics, we work toward building computational capabilities that position PNNL as a computing powerhouse. We also focus on enhancing the Science of Computing to achieve high-performance, power-efficient, and reliable computing at extreme scales for a spectrum of scientific endeavors that address significant problems of national interest, especially among PNNL’s core pursuits—energy, the environment, national security, and fundamental science.

The Speed to Solution

For scientists in PNNL’s Advanced Computing, Mathematics, and Data Division, their work often crosscuts many domain science sectors within the Laboratory and among external collaborators. In this case, seeking methods to enhance data analytics of biological sequences using algorithmic graph theory led to a distinct intersection with work being done for high-performance computing applications contending with obstacles related to power constraints and massive data movement. For the scientists and their partners involved in this research, one point rings true: in science, the problems you start with may not be the only ones you solve.

Addressing Research Priorities at the Big Data Strategic Initiative Workshop

Kerstin Kleese van Dam, the Data Services Team Lead within PNNL’s Advanced Computing, Mathematics, and Data Division, will be among a small number of invited attendees at the 2015 Federal Big Data Strategic Initiative Workshop, or BDSI-2015, being held at Georgetown University in Washington D.C., on Friday, January 23, 2015. The one-day workshop will unite researchers and industry partners working on Big Data challenges and technologies to provide their expertise, perspective, and vision toward crafting viable guidelines and priorities for federal agencies seeking to develop and expand their respective Big Data research initiatives.  

Halappanavar to Lead Analysis and Algorithms Team

Congratulations to Mahantesh Halappanavar, who was named new Team Lead of the Analysis and Algorithms group within FCSD’s Advanced Computing, Mathematics, and Data Division’s Data Sciences pillar. Mahantesh has long been a contributor to the many efforts at PNNL that use algorithmic techniques to solve scientific computing problems. His interest in graph algorithms applied to high-performance computing has resulted in novel research affecting areas spanning graph matching and coloring to combinatorial algorithms to stochastic coordinate descent and community detection.

The Dynamics of Mixing

Analyzing what happens where interfaces mingle is essential toward understanding and controlling fundamental mechanisms in both natural and industrial systems, especially for improving the quality and usability of models that measure reactive transport in CO2 storage, subsurface flow and transport, or mixing-driven biochemical processes in filters and/or living tissues. By considering the reaction front of heterogeneous fluid flows in porous media, whose reaction rates are sharply influenced by compression and diffusion, researchers developed a new model for predicting reaction front kinetics in these flows that provides a more complete assessment regarding the effects of many processes—stretching, coalescence, and fluid particle dispersion—on reactive transport dynamics.

Addressing Irregular Applications

Once again, Dr. John Feo and Dr. Antonino Tumeo, of PNNL’s Advanced Computing, Mathematics, and Data Division, were chosen to serve as guest editors. This time, they will team up for a special edition of Parallel Computing devoted to “Theory and Practice of Irregular Applications (TaPIA).” Parallel Computing is an international journal that centers on the practical use of parallel computer systems, including high-performance architecture, system software, programming systems and tools, and applications. Currently, a Call for Papers has been issued and submissions will be accepted through Monday, March 30, 2015. The TaPIA issue, focused on uncovering solutions for supporting efficient design, development, and execution of irregular applications, is expected to be published in November 2015.

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