Dear All, After an initial misunderstanding on my part, we did establish that our FiPy/Trilinos configuration allowed the intended distribution of calculations across multiple processors (at least for the ../examples/diffusion/mesh1D example), after much patient help from Drs. Guyer and Wheeler and Igor.
Moving on to our own code, which at present subclasses Grid1D, we are unable to achieve any decrease in execution time. In fact the opposite: we see a slight increase in all our trials, across an order of magnitude in the number of grid points and 2 orders in time steps. (Sigh...) To help understand what's going on, we have 3 general questions: 1) Is there an example in the FiPy 2.1 release that demontrates a performance improvement from using the trilinos solvers in parallel? (That is, is there an example that runs in a certain amount of time with the pysparse solvers and then less time using mpirun and the trilinos solvers?) I'm hoping to compare the set-up for something that "works" with what we are doing in order to look for clues as to the difference. 2) Just in a very general way, what features would you expect a FiPy problem to have that would lend themselves to improved performance under the parallelization scheme FiPy 2.1 implements? Thanks and regards... +jtg+
