Using Application-Domain Knowledge in the Runtime Support of Multi- Experiment Computational Studies

摘要

Multi-Experiment Studies (MESs) is a type of computational study in which the same simulation software is executed multiple times, and the result of all executions need to be aggregated to obtain useful insight. As computational simulation experiments become increasingly accepted as part of the scienti c process, the use of MESs is becoming more wide-spread among scientists and engineers. MESs present several challenging requirements on the computing system. First, many MESs need constant user monitoring and feedback, requiring simultaneous steering of multiple executions of the simulation code. Second, MESs can comprise of many executions of long-running simulations; the sheer volume of computation can make them prohibitively long to run. Parallel architecture o er an attractive computing platform for MESs. Low-cost, small- scale desktops employing multi-core chips allow wide-spread dedicated local access to parallel computation power, o ering more research groups an opportunity to achieve interactive MESs. Massively-parallel, highperformance computing clusters can a ord a level of parallelism never seen before, and present an opportunity to address the problem of computationally intensive MESs. However, in order to fully leverage the bene ts of parallel architectures the traditional parallel systems' view has to be augmented. Existing parallel computing systems often treat each execution of the software as a black box and are prevented from viewing an entire computational study as a single entity that must be optimized for. This dissertation investigates how a parallel system can view MESs as an end-to-end system and leverage the application-speci c properties of MESs to address its requirements.