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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.

They’ve Got ‘Game’

As part of this year’s IEEE Symposium on Technologies for Homeland Security, known as HST ’15, scientists from PNNL and Virginia Tech were honored with the Cyber Security Track Best Paper Award for their work, “Quantifying Mixed Uncertainties in Cyber Attacker Payoffs.” The paper employs game theory to mathematically address cyber-system security and resilience challenges in the context of added uncertainties. The authors, who represent PNNL’s National Security and Fundamental & Computational Sciences directorates, received their award during the initial HST ’15 Plenary Session on April 14, 2015.

Improving Energy, Performance Efficiency for High Performance Computing

Shuaiwen Leon Song, a research scientist with PNNL’s HPC group, and Chao Li, a Ph.D. student with North Carolina State University who spent time as a research intern at PNNL in 2014, are co-authors of, “Locality-Driven Dynamic GPU Cache Bypassing.” The paper, which presents novel cache optimizations for massively parallel, throughput-oriented architectures, such as GPUs, recently was accepted by the 29th International Conference on Supercomputing and will be presented during the Conference Program in June 2015. According to the authors, their dynamic filter approach affords good performance and energy efficiency improvement with little area and design overhead, making it an important contibution both to the HPC field and industry development.

StreamWorks: Pattern Detection for Your Protection

Detecting cyber security breaches and identifying their attack patterns in complex computing networks as they emerge in real time remains a paramount concern and growing challenge. In their work involving streaming graphs, scientists at PNNL and Washington State University, devised a novel framework, StreamWorks, that categorizes cyber attacks as graph patterns, which then can be examined using a continuous search on a single, large streaming dynamic graph. Identifying events and patterns as they emerge will go a long way in evading and mitigating the computer network intrusions that have potentially criminal, even dangerous, consequences and have made cyber security a multi-billion dollar industry.


Karniadakis Earns 2015 Ralph E. Kleinman Prize

George Em Karniadakis, a joint appointee with PNNL and Brown University, was awarded the Ralph E. Kleinman Prize, sponsored by the Society for Industrial and Applied Mathematics to recognize individual achievement for outstanding research or contributions that bridge the gap between mathematics and applications. Karniadakis will receive the Kleinman Prize during an award ceremony at the International Congress on Industrial and Applied Mathematics being held in Beijing from August 10-14, 2015.

A Meaningful Data Miner

As data sets grow increasingly large and heterogeneous, or “too Big,” their value diminishes if they cannot be mined with precision and purpose. In progressive work involving the Graph Engine for Multithreaded Systems, a multilayer software framework for querying graph databases developed at PNNL, a team of scientists used GEMS to customize commodity, distributed-memory high-performance computing clusters and apply graph algorithms to large-scale data sets on clusters. By incorporating GEMS, HPC query solutions are exploited and results are more predictable. In comparisons with other approaches, GEMS provided noticeable speedups, particularly with larger data sets. This work is featured as part of the March 2015 special issue of Computer devoted to Big Data management.

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