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

Takuma Kawamura, Tomoyuki Noda, Yasuhiro Idomura

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.


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