Joint optimization (JO) of source and mask together is known to produce better SMO solutions than sequential optimization of the source and the mask. However, large scale JO problems are very difficult to solve because the global impact of the source variables causes an enormous number of mask variables to be coupled together. This work presents innovation that minimize this runtime bottleneck. The proposed SMO parallelization algorithm allows separate mask regions to be processed efficiently across multiple CPUs in a high performance computing (HPC) environment, despite the fact that a truly joint optimization is being carried out with source variables that interact across the entire mask.

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