NDLI: Performance Evaluation of Runtime Data Exploration Framework based on In-Situ Particle Based Volume Rendering

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Performance Evaluation of Runtime Data Exploration Framework based on In-Situ Particle Based Volume Rendering

Content Provider	Semantic Scholar
Author	Kawamura, Takuma Noda, Tomoyuki Idomura, Yasuhiro
Copyright Year	2017
Abstract	We examine the performance of the in-situ data exploration framework based on the in-situ Particle Based Volume Rendering (In-Situ PBVR) on the latest many-core platform. In-Situ PBVR converts extreme scale volume data into small rendering primitive particle data via parallel Monte-Carlo sampling without costly visibility ordering. This feature avoids severe bottlenecks such as limited memory size per node and significant performance gap between computation and inter-node communication. In addition, remote in-situ data exploration is enabled by asynchronous file-based control sequences, which transfer the small particle data to client PCs, generate view-independent volume rendering images on client PCs, and change visualization parameters at runtime. In-Situ PBVR shows excellent strong scaling with low memory usage up to ~100k cores on the Oakforest-PACS, which consists of 8,208 Intel Xeon Phi7250 (Knights Landing) processors. This performance is compatible with the remote in-situ data exploration capability.
Starting Page	43
Ending Page	54
Page Count	12
File Format	PDF HTM / HTML
DOI	10.14529/jsfi170302
Volume Number	4
Alternate Webpage(s)	http://superfri.org/superfri/article/download/142/242
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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