2015-2016 Assessment of the Army Research Laboratory Interim Report (2016) / Chapter Skim
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5 Computational Sciences
5 Computational Sciences
Pages 54-64
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From page 54... ...
This chapter evaluates that work, recognizing that it represents only a portion of ARL's Computational Sciences campaign. The Computational Sciences review looked at four distinct areas of research: computing sciences, predictive simulation sciences, data intensive sciences, and advanced computing architectures.
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From page 55... ...
What sets this work apart is that this DBT outperformed all other MEMOCODE entries by a factor of seven, with close to 1.1 cycle efficiency. 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 56... ...
COMPUTING SCIENCES Computing sciences aims to develop the understanding, tools, techniques, and methodologies to fully exploit emerging computing architectures. This is accomplished through the realization of efficient task parallel algorithms and the use of advanced memory hierarchies.
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From page 57... ...
Accomplishments The ARL computing sciences group has established a strategic focus in quantum computing; parallel processing environments for large, heterogeneous parallel systems; and tools to simplify application development for HPC environments. Establishing a coherent and sustaining strategy in these areas was a major accomplishment.
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From page 58... ...
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 59... ...
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. ARL is pioneering the parallelization of hyperreduction methods to bring greater computational efficiency in the simulation of PDE codes.
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From page 60... ...
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 60
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From page 61... ...
Accomplishments Multiscale material modeling is a potentially game-changing computational technology for predictive simulation in the mechanical sciences. The work on multiscale simulations based on scalebridging combines several important threads of cutting-edge research into a useful software product that has the potential to provide higher fidelity deterministic and nondeterministic forward simulations in fluid and solid mechanics.
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From page 62... ...
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. A challenge that cuts across the predictive simulation sciences portfolio is the lack of cuttingedge R&D efforts in validation, verification, and uncertainty quantification.
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From page 63... ...
This is evident from the materials provided by ARL in the area of computing sciences, data-intensive sciences, and predictive simulation sciences. Many of these applications will execute in a battlefield environment, where security and resilience will be of paramount importance.
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From page 64... ...
Strong relationships with key external entities have proven to be valuable across the breadth and depth of ARL research activities. This is an important positive development for ARL, and engagement with additional leading research institutions would further strengthen the awareness and capabilities of ARL researchers in important strategic areas.
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