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  1. null (Ed.)
  2. Scientific simulation workflows executing on very large scale computing systems are essential modalities for scientific investigation. The increasing scales and resolution of these simulations provide new opportunities for accurately modeling complex natural and engineered phenomena. However, the increasing complexity necessitates managing, transporting, and processing unprecedented amounts of data, and as a result, researchers are increasingly exploring data-staging and in-situ workflows to reduce data movement and data-related overheads. However, as these workflows become more dynamic in their structures and behaviors, data staging and in-situ solutions must evolve to support new requirements. In this paper, we explore how the service-oriented concept can be applied to extreme-scale in-situ workflows. Specifically, we explore persistent data staging as a service and present the design and implementation of DataSpaces as a Service, a service-oriented data staging framework. We use a dynamically coupled fusion simulation workflow to illustrate the capabilities of this framework and evaluate its performance and scalability. 
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  3. Summary

    Coupled scientific simulation workflows are composed of heterogeneous component applications that simulate different aspects of the physical phenomena being modeled and that interact and exchange significant volumes of data at runtime. As the data volumes and generation rates keep growing, the traditional disk I/O–based data movement approach becomes cost prohibitive, and workflow requires more scalable and efficient approach to support the data movement. Moreover, the cost of moving large volume of data over system interconnection network becomes dominating and significantly impacts the workflow execution time. Minimize the amount of network data movement and localize data transfers are critical for reducing such cost. To achieve this, workflow task placement should exploit data locality to the extent possible and move computation closer to data. In this paper, we investigate applying in‐memory data staging and data‐centric task placement to reduce the data movement cost in large‐scale coupled simulation workflows. Specifically, we present a distributed data sharing and task execution framework that (1) co‐locates in‐memory data staging on application compute nodes to store data that needs to be shared or exchanged and (2) uses data‐centric task placement to map computations onto processor cores that a large portion of the data exchanges can be performed using the intra‐node shared memory. We also present the implementation of the framework and its experimental evaluation on Titan Cray XK7 petascale supercomputer.

     
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