2015-2016 Assessment of the Army Research Laboratory (2017) / Chapter Skim
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5 Computational Sciences
5 Computational Sciences
Pages 118-128
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From page 118... ...
This chapter evaluates that work, recognizing that it represents only a portion of ARL's Computational Sciences Campaign. ADVANCED COMPUTING ARCHITECTURES Alongside a focused research effort in tactical high-performance computing (HPC)
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From page 119... ...
The work on computation ferrying is exploring an important aspect of realizing ARL's vision of tactical HPC. It considers the tasks that could be computed on handheld devices or on mobile tactical HPC resources.
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From page 120... ...
Challenges and Opportunities In advanced computing architectures, the Computational Sciences Campaign specifically identified the goal of leadership in areas that include heterogeneous computing; many-core integrated architectures; tactical HPC; power, performance, and portability; quantum computing; neurosynaptic computing; and software-defined networking (SDN)
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From page 121... ...
The research in software integration is focused on addressing these challenges and developing an approach for software design that is predicated on the principle of systematic software reuse. Accomplishments The ARL computing sciences group has established a strategic focus in quantum computing; arallel p processing environments for large, heterogeneous parallel systems; and tools to simplify application development for HPC environments.
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From page 122... ...
Related work on adaptive fast multipole methods using task-oriented parallelism suffered from a lack of supporting performance data and insufficient analysis and articulation of scaling properties. Work related to the implementation of a Bayesian quantum game builds an experimental, computational, and theoretical framework for using quantum statistics to model aspects of cognition.
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From page 123... ...
Similarly, the work on neuromorphic computing represents a fresh and original approach. The work related to model order reduction methods for large-scale simulation data is part of a larger collaboration with the Stanford University group on model order reduction.
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From page 124... ...
For example, huge data sets comprising network data, textual interactions, and even audio logs are generated and archived by large training exercises. The collection, curation, and dissemination of such large military-focused data sets could be vital to the ARL research mission as well as future engineering and technology efforts throughout DOD.
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From page 125... ...
PREDICTIVE SIMULATION SCIENCES The research program in predictive simulation sciences reflects an appropriately broad understanding of the underlying science and of comparable R&D activities at other institutions and agencies. Many of the research projects include collaborations with academia, federal laboratories, and industrial partners, and a few already have transition plans in place to deploy the results of the research in appropriate national security settings.
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From page 126... ...
The principal technical challenges for this work lie in the availability of HPC resources, because the smaller scale simulation components can consume prodigious numbers of computational cycles. The research group has worked to overcome this challenge by deploying an optimal sampling scheme to reduce computational burdens on the smaller scale simulation components and by utilizing software broker components to aid in the load-balancing required for optimal use of HPC resources.
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From page 127... ...
Challenges and Opportunities Predictive simulation sciences are increasingly important in DOD R&D venues. A broader understanding of the strengths and weaknesses of modeling and simulation techniques and of their impact on Army R&D practices is essential to help steer ARL research activities toward full realization of future simulation trends.
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From page 128... ...
Over the next decade, big data, complex analytics, and modeling and simulation will all come together in critical ARL applications and environments. This is evident from the materials provided by ARL in the area of computing sciences, data-intensive sciences, and predictive simulation sciences.
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