A Scalable Pipeline for Transcriptome Profiling Tasks with On-Demand Computing Clouds

Shams, Soheil; Kim, Nayong; Meng, Xiangru; Ha, Ming Tai; Jha, Shantenu; Wang, Zhong; Kim, Joohyun

doi:10.1109/ipdpsw.2016.129

Cited by 1 publication

(1 citation statement)

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“…Because of this, processing is increasingly moving to grid-and cloud-based distributed computing facilities. From genetic sequencing [11] and bioinformatics [12] to neuroscience [13] and ecology [14], ever growing datasets have driven the development of distributed workflow systems in science [15] [16].…”

Section: Related Workmentioning

confidence: 99%

An Automated Scalable Framework for Distributing Radio Astronomy Processing Across Clusters and Clouds

Mechev¹,

Oonk²,

Danezi³

et al. 2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2017 — PoS(ISGC2017)

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The Low Frequency Array (LOFAR) radio telescope is an international aperture synthesis radio telescope used to study the Universe at low frequencies. One of the goals of the LOFAR telescope is to conduct deep wide-field surveys. Here we will discuss a framework for the processing of the LOFAR Two Meter Sky Survey (LoTSS). This survey will produce close to 50 PB of data within five years. These data rates require processing at locations with high-speed access to the archived data.To complete the LoTSS project, the processing software needs to be made portable and moved to clusters with a high bandwidth connection to the data archive. This work presents a framework that makes the LOFAR software portable, and is used to scale out LOFAR data reduction. Previous work was successful in pre-processing LOFAR data on a cluster of isolated nodes. This framework builds upon it and and is currently operational. It is designed to be portable, scalable, automated and general. This paper describes its design and high level operation and the initial results processing LoTSS data.

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