Performance prediction for a code with data-dependent runtimes
In this paper we present a predictive performance model for a key biomedical imaging application found as part of the U.K. e-Science Information eXtraction from Images (IXI) project. 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. Copyright (c) 2007 John Wiley & Sons, Ltd.
performance prediction medical image processing cluster computing grid computing