Performance Prediction for a Code with Data-dependant Runtimes

In this paper we present a preliminary predictive model for a key biomedical imaging application in the UK e-Science IXI (Information eXtraction from Images) project [1]. This code represents a significant challenge for our existing performance prediction tools as it has internal structures that exhibit highly variable runtimes depending on qualities in the input data provided. Since the runtime can vary by more than an order of magnitude, it has been difficult to apply meaningful quality of service criteria to workflows that use this code. The model developed here is used in the context of an interactive scheduling system which provides rapid feedback to the users, allowing them to tailor their workloads to available resources, or to allocate extra resources to scheduled workloads.