The normalized Jacobian ll time not including the Jacobian export time Jac Fill  Jac Export Time
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The normalized Jacobian ll time not including the Jacobian export time Jac Fill Jac Export Time

715 The normalized number of nonlinear solves Nonlin Solves The normalized average number of linear iterations Avg Lin Iter The normalized total time not including IO Total Time IO The run times and iteration counts have been normalized by the ru

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The normalized Jacobian ll time not including the Jacobian export time Jac Fill Jac Export Time




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The normalized Jacobian fill time, not including the Jacobian export time (“Jac Fill - Jac Export Time”). 715 The normalized number of nonlinear solves (“# Nonlin Solves”). The normalized average number of linear iterations (“Avg # Lin Iter”). The normalized total time not including I/O (“Total Time - IO”). The run times and iteration counts have been normalized by the run time and iteration count (respec- tively) for the smallest run (8 km GIS with 5 vertical layers, run on 4 cores). Figure 16 reveals that 720 the run times and iteration times scale well, albeit not

perfectly, in a weak sense. (a) (b) Fig. 16. Controlled, weak scalability study on Hopper : (a) Total linear solve, finite element assembly, and total run times in seconds , (b) Additional timing information ( time or # iterations). 6.3.3 Strong scalability for realistic Greenland initial conditions on a variable-resolution mesh For the performance study described in the previous paragraph, the data has been smoothed and the lateral boundary was determined by the coarsest (8 km resolution) mesh. We now perform a 725 scalability study for the GIS interpolating directly original datasets

into the mesh considered. This results in better resolved topography, basal friction and temperature fields. As before, the surface topography and temperature fields are from (Bamber et al., 2013) and were generated as a part of the “Jacobian export time” refers to the time required to transfer (“export”) data from an element-based decomposition, which can be formed with no communication, to a node-based decomposition, where rows of the matrix are uniquely owned by a single processor. 32