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A Performance Evaluation Method for Coupled Models

In the High-Performance Computing context, the performance evaluation of a parallel algorithm is made mainly considering the elapsed time running the parallel application with both different number of cores or different problem sizes (for scaled speed-up). Typically, parallel applications embed mechanisms for efficiently using the allocated resources, guarantying for example a good load balancing and reducing the parallel overhead. Unfortunately, this assumption is not true for coupled models.These models are born from the coupling of stand-alone climate applications. The component models are developed independently from each other and they follow different development roadmaps. Moreover, they are characterized by different levels of parallelization, different requirements in terms of workload and they have their own scalability curve. Considering a coupled model as a single parallel application, we can note the lacking of a policy for balancing the computational load on the available resources. This work tries to address the issues related to performance evaluation of a coupled model, and to answer to the following questions: allocated a given number of processors for the whole coupled model, how to configure the run in order to balance the workload? How many processors must be assigned to each of the component models? The methodology here described has been applied for evaluating the scalability of the CMCC-MED coupled model designed by INGV and the ANS Division of the CMCC. The evaluation has been carried out on two different computational architectures: a scalar cluster based on IBM Power6 processors; and a vector cluster based on NEC-SX9 processors.