We present an implementation of parallel I∕O in the Modular Ocean Model (MOM), a numerical ocean model used for climate forecasting, and determine its optimal performance over a range of tuning parameters. Our implementation uses the parallel API of the netCDF library, and we investigate the potential bottlenecks associated with the model configuration, netCDF implementation, the underpinning MPI-IO library/implementations and Lustre filesystem. We investigate the performance of a global 0.25° resolution model using 240 and 960CPUs. The best performance is observed when we limit the number of contiguous I∕O domains on each compute node and assign one MPI rank to aggregate and to write the data from each node, while ensuring that all nodes participate in writing this data to our Lustre filesystem. These best-performance configurations are then applied to a higher 0.1° resolution global model using 720 and 1440CPUs, where we observe even greater performance improvements. In all cases, the tuned parallel I∕O implementation achieves much faster write speeds relative to serial single-file I∕O, with write speeds up to 60 times faster at higher resolutions. Under the constraints outlined above, we observe that the performance scales as the number of compute nodes and I∕O aggregators are increased, ensuring the continued scalability of I∕O-intensive MOM5 model runs that will be used in our next-generation higher-resolution simulations.
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