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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.

A Congress of Computational Materials Engineering

During this year’s 3rd World Congress on Integrated Computational Materials Engineering, or ICME 2015, scientists from PNNL’s Applied Computational Mathematics and Engineering group will be active contributors featured during several of the event’s technical program and poster sessions. ACME’s Team Lead and PNNL Laboratory Fellow, Xin Sun, who also is an ICME 2015 World Congress organizer, along with PNNL scientists Kyoo Sil Choi, Xiaohua Hu, Wei Xu, Kevin Lai, and Dangxin Wu, will share their diverse work modeling, simulating, and experimenting with materials engineering processes. ICME 2015 takes place May 31-June 4, 2015 in Colorado Springs, Colorado.



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Interior Design for Supercomputers

Working with computer scientists from Intel Corp., Roberto Gioiosa, a scientist with PNNL’s High Performance Computing group, examined hierarchical approaches to manage high-performance computing resources. The paper describing their work, “Analyzing System Calls in Multi-OS Hierarchical Environments,” recently was accepted by the 2015 International Workshop on Runtime and Operating Systems for Supercomputers (ROSS). In the paper, they describe a framework that provides supporting data to inform OS and runtime designers on where to implement OS system calls. Gioiosa and his Intel colleagues will present their work during the ROSS 2015 Operating Systems Session on Tuesday, June 16, 2015 in Portland, Oregon.



Energy Star

To improve overall energy efficiency and performance on future exascale computing systems, scientists from PNNL; University of California, Riverside; and Marquette University examined some advanced high-performance computing systems and determined undervolting that also leverages existing mainstream resilience techniques at scale to reduce power consumption improved system failure rates. The undervolting method does not require modifying existing hardware or using pre-production machines and has shown positive results toward achieving a cost-efficient, energy-savings implementation for the HPC field. The paper documenting this first-of-its-kind work, “Investigating the Interplay between Energy Efficiency and Resilience in High Performance Computing,” will be presented during the IPDPS 2015 conference’s technical program.



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.



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