Vendredi 9 décembre – 14h30 – salle Ada Lovelace
Titre : Load Balancing for Coupled Simulations
Résumé : In the field of scientific computing, the load balancing is an important step conditioning the performance of parallel programs. The goal is to distribute the computational load across multiple processors in order to minimize the execution time. This is a well-known problem that is unfortunately NP-hard. The most common approach to solve it is based on graph or hypergraph partitioning method, using mature and efficient software tools such as Metis, Zoltan or Scotch.
Nowadays, numerical simulation are becoming more and more complex, mixing several models and codes to represent different physics or scales. Here, the key idea is to reuse available legacy codes through a coupling framework instead of merging them into a standalone application. For instance, the simulation of the earth’s climate system typically involves at least 4 codes for atmosphere, ocean, land surface and sea-ice . Combining such different codes are still a challenge to reach high performance and scalability. In this context, one crucial issue is undoubtedly the load balancing of the whole coupled simulation that remains an open question. The goal here is to find the best data distribution for the whole coupled codes and not only for each standalone code, as it is usually done. Indeed, the naive balancing of each code on its own can lead to an important imbalance and to a communication bottleneck during the coupling phase, that can dramatically decrease the overall performance. Therefore, one argues that it is required to model the coupling itself in order to ensure a good scalability, especially when running on tens of thousands of processors. In this work, we develop new algorithms to perform a coupling-aware partitioning of the whole application.
