On a Generalized Approach to Order-Independent Image Composition in Parallel Visualization
Document Type
Conference Presentation
Department
Computer Science
Conference Title
2013 IEEE International Performance Computing and Communications Conference
Location
San Diego, CA
Conference Dates
December 6-8, 2013
Date of Presentation
12-6-2013
Abstract
Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel visualization. Among the three well-known parallel architectures, i.e. sort-first/middle/last, sort-last, which comprises of two stages, i.e. image rendering and composition, is often preferred due to its adaptability to load balancing. We propose a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition in sort-last parallel rendering. GMPL is of two-fold novelty: i) it takes a prime factorization-based approach for processor grouping, which not only obviates the common restriction in existing methods on the total number of processors to fully utilize computing resources, but also breaks down processors to the lowest level with a minimum number of peers in each group to achieve high concurrency and save communication cost; ii) within each group, it employs an improved direct send method to narrow down each processor's pairing scope to further reduce communication overhead and increase composition efficiency. The performance superiority of GMPL over existing methods is evaluated through rigorous theoretical analysis and further verified by extensive experimental results on a high-performance visualization cluster.
DOI
10.1109/PCCC.2013.6742798
Recommended Citation
Chu, D.,
Wu, Q.,
Gao, J.,
&
Wang, L.
(2013).
On a Generalized Approach to Order-Independent Image Composition in Parallel Visualization.
Paper presented at 2013 IEEE International Performance Computing and Communications Conference in San Diego, CA.
https://scholarlycommons.pacific.edu/soecs-facpres/69
