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**Up:** 8.2.1 Computation Efficiency
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After the optimizer successfully identified an improved set of
parameters, it is necessary to compute the Jacobian of the
**model**. This means at least *one evaluation* of the **model**
must be performed for *each parameter* which is selected for
optimization. The number of system jobs will therefore be a multiple
(times the number of free parameters) compared to the test evaluation
phase. Since all **model** evaluations are independent from each other,
and so are their associated system jobs, they can be processed in
parallel. In contrast to single evaluation there will be excess system
jobs compared to the number of workstations and as a consequence, job
farming will use any available workstation as far as allowed
(depending on their load limit) and take care that neither a
workstation is overloaded nor is underemployed.
Gradient computation impressively demonstrates the power of
*SIESTA*'s job farming capabilities. Unless job farming were
available for an optimization, the whole procedure would require large
amounts of real time. Job farming scales down computation time
linearly during this phase of an optimization. *SIESTA* was
successfully employed for a rigorous optimization of doping
profiles [O10], where 62 (!) parameters of a discretized doping
profile were optimized. The optimization took approximately one day on
a cluster of 20 workstations.

** Next:** 8.2.2 System Scalability
**Up:** 8.2.1 Computation Efficiency
** Previous:** 8.2.1.1 Test Evaluation Phase
*Rudi Strasser *

1999-05-27