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Sökning: WFRF:(Gropp William)

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1.
  • Huerta, E. A., et al. (författare)
  • Enabling real-time multi-messenger astrophysics discoveries with deep learning
  • 2019
  • Ingår i: Nature reviews physics. - : Springer Science and Business Media LLC. - 2522-5820. ; 1:10, s. 600-608
  • Forskningsöversikt (refereegranskat)abstract
    • Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics. A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.
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2.
  • Karp, Martin, 1996- (författare)
  • Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems : Architectures, Algorithms, and Applications
  • 2024
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Direct numerical simulations (DNS) of turbulence have a virtually unbounded need for computing power. To carry out these simulations, software, computer architectures, and algorithms must operate as efficiently as possible to amortize the large computational cost. However, in a computing landscape increasingly incorporating heterogeneous computer systems, changes are necessary. In this thesis, we consider how DNS can be carried out efficiently on upcoming heterogeneous computer systems. This work relates to developing algorithms for upcoming heterogeneous computer architectures, overcoming software challenges associated with large-scale DNS on these platforms, and applying these developments to new flow cases that were previously too costly to carry out. We consider in particular the spectral element method for DNS and evaluate how this method maps to field-programmable gate arrays, graphics processing units, as well as conventional processors. We also consider the issue of trading arithmetic operations for less communication, reducing the cost of solving the linear systems that arise in the spectral element method. Our developments are incorporated into the spectral element framework Neko, enabling Neko to strong-scale efficiently on the largest supercomputers in the world. Finally, we have carried out several DNS such as the simulation of a Flettner rotor in a turbulent boundary layer and simulating Rayleigh-Bénard convection at very high Rayleigh numbers. The developments in this thesis enable the high-fidelity simulation of turbulence on emerging computer systems with high parallel efficiency and performance.
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