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You can run Gaussian jobs in parallel using the shared-memory model only. Distributed memory is not supported because Linda, the tool used by Gaussian to distribute a computation over multiple nodes, is not available. In order to run a Gaussian computation in parallel on N CPUs, you need to specify the number of cores in the Gaussian input file. Please note that the memory size indicated in Gaussian's input file is for the whole computation, whereas the size indicated in the bsub command is per CPU. This difference does not matter if you are using only one CPU. However, if you are doing a parallel computation, you must adjust the size accordingly. Let's assume that your computation needs 4 CPUs and 8 GB (8192 MB) of memory:


You should therefore request 8192 MB / 4 CPUs + 25% safety margin = 2560 MB / CPU, hence:

bsub -n 4 -R "rusage[mem=2560]" ...