Hi all,
As part of an Exascale project I’ve been asked to gather information on biomolecular simulation code performance in terms of numerical methods. Specifically how - for typical large (c 1M atom) problem sizes – codes divide their time between the “numerical dwarves”. For reference, these are a) Dense linear algebra, b) Sparse linear algebra, c) Spectral. Methods (e.g. FFT), d) N-body methods, e) Structured grids, f) Unstructured grids, g) MapReduce, h) Combinational logic, i) Graph traversal, j) Dynamic programming, k) Backtrack & Branch-and-bound, l) Graphical models, and m) Finite state machines. What percentage of a typical run would be spent doing each of these?
Does anyone have any data on this for the latest releases of pmemd.cuda, in particular?
Many thanks for all insights!
Charlie Laughton
School of Pharmacy
University of Nottingham, UK
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Received on Wed Oct 04 2023 - 01:00:02 PDT
