Towards autonomic application-sensitive partitioning for SAMR applications

Distributed structured adaptive mesh refinement (SAMR) techniques offer the potential for accurate and cost-effective solutions of physically realistic models of complex physical phenomena. However, the heterogeneous and dynamic nature of SAMR applications results in significant runtime management challenges. This paper investigates autonomic application-sensitive SAMR runtime management strategies and presents the design, implementation, and evaluation of ARMaDA, a self-adapting and optimizing partitioning framework for SAMR applications. ARMaDA monitors and characterizes application runtime state, and dynamically selects and invokes appropriate partitioning mechanisms that match current SAMR state and optimize its computational and communication performance. The advantages of the autonomic partitioning capabilities provided by ARMaDA are experimentally demonstrated.