Autonomic Malleability in Iterative MPI Applications

During their execution, a significant number of applications often sub utilize the capacity of the resources to which they are allocated or require more. Furthermore, with the current scale up trend in server design, effective utilization can only be achieved by applications sharing such resources. Cluster management systems already support static resource partitioning at job submission time and given that application utilization more than often varies during the execution, it will become increasingly more important to permit applications to harness all available spare capacity. This paper investigates the feasibility of malleable evolving versions of applications to improve performance and system efficiency. Extending a previous classification, we show that improvements can be achieved for a real astrophysics application.