Improvements in SDPD methodology have meant that ever more complex structures are being tackled using direct space methods.  As a very general rule of thumb, the more complex the structure, the more difficult it is to locate the global minimum in the direct space search.  This difficulty can, to some extent, be circumvented by running many instances of the search; for stochastic search methods such as simulated annealing, each instance can be run independently of any other.  Such search problems are therefore ideally suited to disposition on a distributed grid-type system that makes use of existing compute resources on a network.  At the Rutherford Appleton Laboratory, we have adapted the DASH structure determination code to run on a Univa UD GridMP system in order to distribute simulated annealing runs across hundreds of computer simultaneously with excellent scaling.

We will illustrate the use of this approach with some challenging Z'=4 structures and show how the approach usually means 'better' results rather than simply 'faster', even when faster equates to more than two orders of magnitude when compared to execution on a single computer.  The principles outlined are equally well applicable to other global-optimisation based structure determination codes and to other grid-type systems, such as the widely used and freely available CONDOR system.

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