Approximate Run Times on the Cluster

The table shows example problem sizes and the time it took to execute them using different numbers of CPU's

Each problem had only one frequency point.

What you will notice is that there is a point where adding more CPU'S does not reduce the execution time significantly.

This is because the network overheads increase as you add more CPU's and at one point a new network segment is added.

So how should you use the table?

  1. See what size problem you have
  2. Make sure you have chosen enough CPU's to solve the problem (800MB per CPU*)
  3. The table will provide an estimate of the execution time and speedup you could achieve should you wish to use more CPU's than the minimum required.
  4. The total execution time will approximately be the execution time in the table multiplied by the number of frequency points in your problem.

*Note there are 2 CPU's per node.

Credit usage estimate
Problem size 3.549GB 5.337GB 16.855GB 40.769GB 84.779GB
10 CPU's (problems up to 9GB) 8:33 min 14:42 min - - -
20 CPU's ( problems up to 18GB) 5:41 min 8:56 min 36:17 min - -
30 CPU's (problems up to 27GB) 4:39 min 7:31 min 27:54 min - -
40 CPU's (problems up to 36GB) 4:34 min 7:04 min 25:50 min - -
50 CPU's (problems up to 45GB) 4:33 min 7:01 min 23:34 min 69.23 min -
60 CPU's (problems up to 54GB) 4:22 min 6:39 min 22:26 min 62.37 min -
70 CPU's (problems up to 63GB) 4:41min 6:57 min 22:15 min 64.52 min -
80 CPU's (problems up to 72GB) 4:32 min 6:49 min 21:19 min 60.19 min -
90 CPU's (problems up to 81GB) 4:48 min 7:05min 22:06 min 60.02 min -
98 CPU's (problems up to 88GB) 4:44min 6:53min 21:38 min 58.39 min 140.53 min
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