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Memory Efficient Load Balancing for Distributed Large-Scale Volume Rendering Using a Two-Layered Group Structure
Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0003-0639-0639
Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
2019 (English)In: IEICE transactions on information and systems, ISSN 0916-8532, E-ISSN 1745-1361, Vol. E102D, no 12, p. 2306-2316Article in journal (Refereed) Published
Abstract [en]

We propose a novel compositing pipeline and a dynamic load balancing technique for volume rendering which utilizes a two-layered group structure to achieve effective and scalable load balancing. The technique enables each process to render data from non-contiguous regions of the volume with minimal impact on the total render time. We demonstrate the effectiveness of the proposed technique by performing a set of experiments on a modern GPU cluster. The experiments show that using the technique results in up to a 35.7% lower worst-case memory usage as compared to a dynamic k-d tree load balancing technique, whilst simultaneously achieving similar or higher render performance. The proposed technique was also able to lower the amount of transferred data during the load balancing stage by up to 72.2%. The technique has the potential to be used in many scenarios where other dynamic load balancing techniques have proved to be inadequate, such as during large-scale visualization.

Place, publisher, year, edition, pages
2019. Vol. E102D, no 12, p. 2306-2316
Keywords [en]
large-scale visualization, distributed computing, load balancing, GPU
National Category
Computer and Information Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-265514DOI: 10.1587/transinf.2019PAP0003ISI: 000499697000004Scopus ID: 2-s2.0-85076443726OAI: oai:DiVA.org:kth-265514DiVA, id: diva2:1378038
Note

QC 20191213

Available from: 2019-12-13 Created: 2019-12-13 Last updated: 2020-01-08Bibliographically approved

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Markidis, Stefano

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