Hi, I'm trying to speed up the Cahn-Hilliard example by running it in parallel but the running time is always the same regardless of how many processors I use. I'm using FiPy in Linux installed from Anaconda (I tried the same installation in OsX but trillinos doesn't work) following the instructions on the website. I ran the parallel.py example and the output seems to indicate that trilinos is working and correctly communicating with mpi. I'm running a 500x500 grid but I tried changing the size and I don't see any speedup by running it in parallel, as if each thread was integrating the whole grid. Any ideas?
Best, Adrian. _______________________________________________ fipy mailing list fipy@nist.gov http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
