A multi-level parallel approach to increase the computation efficiency of a global ocean temperature dataset reconstruction

There is an increasing need to provide real-time datasets for climate monitoring and applications. However, the current data products from all international groups have at least a month delay for data release. One reason for this delay is the long computing time of the global reconstruction algorithm (so-called mapping approach). To tackle this issue, this paper proposes a multi-level parallel computing model to improve the efficiency of data construction by parallelization of computation, reducing code branch prediction, optimizing data spatial locality, cache utilization, and other measures. This model has been applied to a mapping approach proposed by the Institute of Atmospheric Physics (IAP), one of the world's most widely used data products in the ocean and climate field. Compared with the traditional serial construction of MATLAB-based scheme on a single node, the speed of the construction after parallel optimizations is speeded up by ∼4.7 times. A large-scale parallel experiment of a long-term (∼1000 months) gridded dataset utilizing over 16,000 processor cores proves the model's scalability, improving ∼1200 times. In summary, this new model represents another example of the application of high-performance computing in oceanography and climatology.