Adaptive Scientific Workflows
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Large computing systems including clusters, clouds, and grids, provide high-performance capabilities that can be utilized for scientific applications. As the ubiquity of these systems increases and the scope of analysis performed on them expand, there is a growing need for applications that do not require users to learn the details of high-performance computing (HPC), and are flexible and adaptive to accommodate the best time-to-solution. In this dissertation we introduce a new adaptive capability for the MeDICi middleware and describe the applicability of this design to a scientific workflow application for biology. This adaptive framework provides a programming model for implementing a scientific workflow using high-performance systems and choosing configuration options at run-time, automatically reacting to HPC load fluctuations. In production multi-user high-performance (HPC) batch computing environments, wait times for scheduled jobs are highly dynamic. For scientific users, the primary measure of efficiency is wall clock time-to-solution. In high throughput applications, such as many kinds of biological analysis, the computational work to be done can be flexibly scheduled taking a longer time on a small number of processors or a shorter time on a large number of processors. Therefore the capability to choose a platform at run-time based on both processing capabilities and availability (lowest wait time) would be attractive. The goal of our work was to create an adaptive interface to HPC systems that dynamically reschedules high-throughput calculations in response to fluctuating load, optimizing for time-to-solution. This was done by implementing middleware functionality to (1) monitor the resource load on a given compute cluster, (2) generate a plan, checking on the applicability of the plan with the defined goals and (3) adaptively choosing the optimal job dimensions (number of processors and wall-clock time) to provide the best time-to-solution results.