Performance evaluation of runtime data exploration framework based on in-situ particle based volume rendering

Kawamura, Takuma; Noda, Tomoyuki; Idomura, Yasuhiro

Supercomputing Frontiers and Innovations, 4(3), p.43 - 54, 2017/07

AA2017-0206.pdf:3.74MB

https://doi.org/10.14529/jsfi170302

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 about 100k cores on the Oakforest-PACS, which consists of 8,208 Intel Xeon Phi7250 (Knights Landing) processors.