Section: New Results

Efficient algorithmic for load balancing and code coupling in complex simulations

Dynamic load balancing for massively parallel coupled codes

In the field of scientific computing, load balancing is a major issue that determines the performance of parallel applications. Nowadays, simulations of real-life problems are becoming more and more complex, involving numerous coupled codes, representing different models. In this context, reaching high performance can be a great challenge. In the PhD of Maria Predari (started in october 2013), we develop new graph partitioning techniques, called co-partitioning, that address the problem of load balancing for two coupled codes: the key idea is to perform a coupling-aware partitioning, instead of partitioning these codes independently, as it is usually done. However, our co-partitioning technique requires to use graph partitioning with fixed vertices, that raises serious issues with state-of-the-art software, that are classically based on the well-known recursive bisection paradigm (RB). Indeed, the RB method often fails to produce partitions of good quality. To overcome this issue, we propose a new direct $k$-way greedy graph growing algorithm, called KGGGP, that overcomes this issue and succeeds to produce partition with better quality than RB while respecting the constraint of fixed vertices. Experimental results compare KGGGP against state-of-the-art methods for graphs available from the popular DIMACS'10 collection. This work will be presented in the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2016).