In‐memory staging and data‐centric task placement for coupled scientific simulation workflows. (18th April 2017)

• Record Type: Journal Article
• Authors: Zhang, Fan; Jin, Tong; Sun, Qian; Romanus, Melissa; Bui, Hoang; Klasky, Scott; Parashar, Manish
• Is Part Of: Concurrency and computation. Volume 29:Number 12(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-04-18
• Subjects: coupled simulations -- data‐intensive application workflows -- data‐centric task mapping -- data staging; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4147
• Languages: English
• ISSNs: 1532-0626

Abstract

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.