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Efficient in-situ workflows for time-critical applications on heterogeneous ecosystems Item
In-situ workflows are a special class of scientific workflows, where different component applications (such as simulation, visualization, analysis) run concurrently, and data flows continuously between components during the whole workflow lifetime. Traditionally, simulations write large amounts of output data to persistent storage, which are later read for future analysis/visualization. In comparison, in-situ workflows allow analysis/visualization components to consume simulation data while the simulations are still running and thus reduce the I/O overhead. There are recent research works that focus on providing data transport libraries to help compose a group of applications into an integral in-situ workflow. However, only a few ``performance-oriented'' studies exist for in-situ workflows, and most of these works focus on workflows with simple structures (e.g., single producer and single consumer), also without consideration of heterogeneous environments for in-situ workflows. Being able to efficiently utilize heterogeneous computing resources such as multiple Clouds and HPCs can significantly accelerate real-world in-situ workflows, and benefit applications that require both significant computation power and real-time outputs(e.g., identifying abnormal patterns in fluid dynamics). The goal of this dissertation is to provide resource planning algorithms and runtime support, to improve in-situ workflow performance on heterogeneous environments.
This dissertation first investigates the emerging applications of in-situ workflows, which usually include parallel simulation, visualization, and analysis components. Two representative real-world in-situ workflows are studied in details-- a real-time CFD machine learning/visualization workflow and a wildfire spreading workflow. These workflows showcase the capability of in-situ workflows: e.g., decoupled and accelerated computation and fast near-real-time response time, however, there is a lack of resource planning and runtime support for general in-situ workflows. For resource planning, I first formulate the optimization problem, and then design and implement a heuristic algorithm called ``SNL'' (Scheduled-Neighbor-Lookup). SNL considers the pipelined execution pattern of in-situ workflows, and guides the resource planning of complex in-situ workflows to achieve higher workflow throughput. For the runtime support, I design and implement the ``INSTANT'' runtime framework, a runtime framework to configure, plan, launch, and monitor in-situ workflows for distributed computing environments. INSTANT provides intuitive interfaces to compose abstract in-situ workflows, manages in-site and cross-site data transfers with ADIOS2, and supports resource planning using profiled performance data. Experiments with the two use cases show that INSTANT can efficiently streamline the orchestration of complex in-situ workflows, and the resource planning capability allows INSTANT to plan and carry out fast workflow execution at different computing resource availabilities.
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- Doctor of Philosophy
- Computer Science
- West Lafayette