NDLI: A Diffusion-Based Processor Reallocation Strategy for Tracking Multiple Dynamically Varying Weather Phenomena

Content Provider	IEEE Xplore Digital Library
Author	Malakar, P. Natarajan, V. Vadhiyar, S.S. Nanjundiah, R.S.
Copyright Year	2013
Description	Author affiliation: Supercomput. Educ. & Res. Centre, Indian Inst. of Sci., Bangalore, India (Vadhiyar, S.S.) \|\| Dept. of Comput. Sci. & Autom., Indian Inst. of Sci., Bangalore, India (Malakar, P.; Natarajan, V.) \|\| Centre for Atmos. & Oceanic Sci., Indian Inst. of Sci., Bangalore, India (Nanjundiah, R.S.)
Abstract	Many meteorological phenomena occur at different locations simultaneously. These phenomena vary temporally and spatially. It is essential to track these multiple phenomena for accurate weather prediction. Efficient analysis require high-resolution simulations which can be conducted by introducing finer resolution nested simulations, nests at the locations of these phenomena. Simultaneous tracking of these multiple weather phenomena requires simultaneous execution of the nests on different subsets of the maximum number of processors for the main weather simulation. Dynamic variation in the number of these nests require efficient processor reallocation strategies. In this paper, we have developed strategies for efficient partitioning and repartitioning of the nests among the processors. As a case study, we consider an application of tracking multiple organized cloud clusters in tropical weather systems. We first present a parallel data analysis algorithm to detect such clouds. We have developed a tree-based hierarchical diffusion method which reallocates processors for the nests such that the redistribution cost is less. We achieve this by a novel tree reorganization approach. We show that our approach exhibits up to 25% lower redistribution cost and 53% lesser hop-bytes than the processor reallocation strategy that does not consider the existing processor allocation.
Starting Page	50
Ending Page	59
File Size	660206
Page Count	10
File Format	PDF
ISBN	9780769551173
ISSN	01903918
DOI	10.1109/ICPP.2013.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-01
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantum cascade lasers Clouds Resource management Algorithm design and analysis Clustering algorithms Meteorology Data analysis cloud tracking redistribution processor reallocation data analysis
Content Type	Text
Resource Type	Article

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