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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.

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Krishnamoorthy Co-Author of IEEE Cluster 2014 Best Student Paper

Sriram Krishnamoorthy, a research scientist and System Software and Applications Team Leader in ACMD Division’s High Performance Computing group, was part of the research team honored with the 2014 Best Student Paper Award during this year’s IEEE Cluster 2014. The conference awards were announced on Sept. 24, 2014. The paper, “Scalable Replay with Partial-Order Dependencies for Message-Logging Fault Tolerance,” examined and presented a novel algebraic framework that improved on an existing scalable message-logging fault tolerance scheme and was co-authored with computer scientists from the University of Illinois at Urbana-Champaign and University of Pittsburgh.



To Protect and Serve

During GraphLab Conference 2014, Sutanay Choudhury, a research scientist with PNNL’s Data Sciences group (ACMD Division), hosted a demonstration showcasing M&Ms4Graphs, a graph analytics framework for cyber security. M&Ms4Graphs uses graph-theoretic models to provide continuous updates on system states as part of enabling a resilient (a system’s ability to function in the face of impediments) cyber infrastructure. The project is one of many backed by PNNL’s Asymmetric Resilient Cybersecurity Initiative and features a diverse team of computer scientists and mathematicians from both PNNL’s Fundamental & Computational Sciences and National Security directorates, including major contributors Peter Hui, Kiri Oler, Chase Dowling, Emilie Hogan, Mahantesh Halappanavar, and Sherman Beus.



Alex Tartakovsky

Tartakovsky Appointed New Associate Division Director for Computational Mathematics

This month, Dr. Alexandre Tartakovsky joins the Advanced Computing, Mathematics, and Data Division as the full-time Associate Division Director for Computational Mathematics. In his new role, Alex will oversee the talented personnel who compose ACMD Division’s Computational Mathematics group, which includes computational engineering, uncertainty quantification, multiscale mathematics, and computational social sciences teams. Alex’s goal is to continue building PNNL’s Computational Mathematics group to world-class strength. He also will continue his scientific leadership role in applied mathematics through a variety of important projects.



Airborne particle illustration

SEAK Program Seeks to Tackle the Toughest Processing Problems

In a demonstration of the Laboratory's ongoing commitment to enhancing the Science of Computing by achieving high-performance, power-efficient, and reliable computing at extreme scales, scientists from PNNL's HPC group have joined researchers from the U.S. Army Research Communications-Electronics Research, Development, and Engineering Center (Night Vision and Electronic Sensors Directorate) and the Defense Advanced Research Projects Agency (Microsystems Technology Office) as part of the Suite of Embedded Applications and Kernels, or SEAK, program. SEAK's primary goal is to advance the capabilities of high-performance embedded computing applications for U.S. Department of Defense purposes. PNNL is handling all of the SEAK program's technical aspects, led by Adolfy Hoisie, the project's principal investigator.



Comm Link

Tensor contractions, generalized matrix multiplications that are time-consuming to calculate, make them among the most compute-intensive operations in several ab initio computational quantum chemistry methods. In this work, Sriram Krishnamoorthy, a research scientist with ACMD’s High Performance Computing group, along with scientists from The Ohio State University, developed a systematic framework that uses three fundamental communication operators—recursive broadcast, rotation, and reduction—to derive communication-efficient algorithms for distributed contraction of arbitrary dimensional tensors on the IBM Blue Gene/Q Mira supercomputer. The framework automatically models potential space-performance trade-offs to optimize the communication costs incurred in executing tensor contractions on supercomputers. The paper documenting this work, “Communication-optimal Framework for Contracting Distributed Tensors,” is an SC14 Best Paper award finalist.



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