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Paper: Producing an Infrared Multiwavelength Galactic Plane Atlas Using Montage, Pegasus, and Amazon Web Services
Volume: 485, Astronomical Data Analysis Software and Systems XXIII
Page: 211
Authors: Rynge, M.; Juve, G.; Kinney, J.; Good, J.; Berriman, B.; Merrihew, A.; Deelman, E.
Abstract: In this paper, we describe how to leverage cloud resources to generate large-scale mosaics of the galactic plane in multiple wavelengths. Our goal is to generate a 16-wavelength infrared Atlas of the Galactic Plane at a common spatial sampling of 1 arcsec, processed so that they appear to have been measured with a single instrument. This will be achieved by using the Montage image mosaic engine process observations from the 2MASS, GLIMPSE, MIPSGAL, MSX and WISE datasets, over a wavelength range of 1 μm to 24 μm, and by using the Pegasus Workflow Management System for managing the workload. When complete, the Atlas will be made available to the community as a data product. We are generating images that cover ±180° in Galactic longitude and ±20° in Galactic latitude, to the extent permitted by the spatial coverage of each dataset. Each image will be 5°x5° in size (including an overlap of 1° with neighboring tiles), resulting in an atlas of 1,001 images. The final size will be about 50 TBs. This paper will focus on the computational challenges, solutions, and lessons learned in producing the Atlas. To manage the computation we are using the Pegasus Workflow Management System, a mature, highly fault-tolerant system now in release 4.2.2 that has found wide applicability across many science disciplines. A scientific workflow describes the dependencies between the tasks and in most cases the workflow is described as a directed acyclic graph, where the nodes are tasks and the edges denote the task dependencies. A defining property for a scientific workflow is that it manages data flow between tasks. Applied to the galactic plane project, each 5 by 5 mosaic is a Pegasus workflow. Pegasus is used to fetch the source images, execute the image mosaicking steps of Montage, and store the final outputs in a storage system. As these workflows are very I/O intensive, care has to be taken when choosing what infrastructure to execute the workflow on. In our setup, we choose to use dynamically provisioned compute clusters running on the Amazon Elastic Compute Cloud (EC2). All our instances are using the same base image, which is configured to come up as a master node by default. The master node is a central instance from where the workflow can be managed. Additional worker instances are provisioned and configured to accept work assignments from the master node. The system allows for adding/removing workers in an ad hoc fashion, and could be run in large configurations. To-date we have performed 245,000 CPU hours of computing and generated 7,029 images and totaling 30 TB. With the current set up our runtime would be 340,000 CPU hours for the whole project. Using spot m2.4xlarge instances, the cost would be approximately $5,950. Using faster AWS instances, such as cc2.8xlarge could potentially decrease the total CPU hours and further reduce the compute costs. The paper will explore these tradeoffs.
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