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Kawamura, Takuma; Hasegawa, Yuta; Idomura, Yasuhiro
Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.187 - 192, 2020/10
In order to realize the atmospheric dispersion prediction of pollutants, a fluid simulation by adaptive mesh refinement (AMR) optimized for GPU supercomputer has been developed, and interactive visualization and parameter steering of the simulation results are needed. In this study, we extend particle-based in-situ visualization method for structured grids into AMR, and enables in-situ steering of the simulation parameters by utilizing an in-situ control mechanism via files. By combining the developed method with plume dispersion simulation in urban areas running on a GPU platform, it was shown that human-in-the-loop pollution source search is possible without enormous parameter scanning.
Kawamura, Takuma; Noda, Tomoyuki; Idomura, Yasuhiro
Supercomputing Frontiers and Innovations, 4(3), p.43 - 54, 2017/07
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.
Kawamura, Takuma
no journal, ,
I was invited from the Science Council of Japan for an invited lecture on In-Situ PBVR visualization technology. To develop visualization technology for future Exascale simulation, the following three problems are bottlenecks. (1) Increase of communication cost under high parallel environment. (2) Low interactivity due to offline visualization for batch processing. (3) Visualization of multivariate data. In order to solve these problems, I developed the following three technologies. (1) Parallel volume rendering with high scalability by PBVR. (2) Framework that enables interactive operation by file-based control via storage. (3) Multidimensional transfer function generation technique for multivariate data. Applying the proposed method to fuel melting simulation JUPITER about 100 million degrees of freedom, we demonstrated the high scalability of visualization processing, the interactive operation at batch processing execution, and the effectiveness of multivariate visualization function.
Kawamura, Takuma
no journal, ,
I presented the research result of "Remote Interactive In-Situ Visualization using Particle Data for Volume Visualization" founded by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN), 2017. With increasing simulation scale, the conventional visualization process involving data transfer to the pre-post node requires a huge amount of work time. To enable real-time visualization and analysis of large-scale simulation, an interactive In-Situ visualization system using particle data for visualization was constructed in this research and this system was ported to the latest GPGPU, XeonPhi, and FX100 environments. Using this system, we confirmed that interactive visualization of batch processing simulations is possible.
Kawamura, Takuma; Idomura, Yasuhiro
no journal, ,
In-Situ PBVR using Particle-Based Volume Rendering (PBVR) is a framework that enables interactive visualization of simulations on supercomputers, and synthesis of volume data and transfer function by user specified algebraic equation by means of computer algebra system. Simulations are accelerated by the appearance of many core architecture, and optimization of in-situ PBVR is also required. We constructed a novel computer algebra system using reverse Polish notation for SIMD optimization of In-Situ PBVR. We applied the proposed method to fuel melting simulation in the nuclear field, and visualization performance was improved by more than 10 times on the latest KNL architecture.
Kawamura, Takuma
no journal, ,
I present the research result of "Remote Interactive In-Situ Visualization using Particle Data for Volume Visualization" supported by the Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN), 2017. With the increase of simulation scale, the conventional visualization process involving data transfer to the pre-post node requires a huge processing cost. To enable real-time visualization and analysis of large-scale simulations, an interactive In-Situ visualization system using particle data for visualization is optimized for the latest SIMD platforms such as XeonPhi, and FX100. Using this system, interactive visualization at runtime is enabled for batch processing simulations.
Kawamura, Takuma
no journal, ,
We discuss the development and application of an in-situ visualization system using particle based visualization data. As the simulation becomes larger, the conventional visualization method involving data transfer to the pre-post node requires a huge amount of processing time. In this study, in order to enable real-time visualization and analysis for large-scale simulations, an interactive in-situ visualization system using particle data was developed and optimized for the latest many-core environment. Using the developed system, it was confirmed that interactive visualization is possible when executing batch processing of simulations. A visualization example of AMR based simulations on the latest GPGPU machine is also reported.
Kawamura, Takuma; Hasegawa, Yuta
no journal, ,
In a large-scale simulation that uses a huge amount of computational resources and runs for a long time on a supercomputer, it is difficult to repeatedly perform recalculation due to adjustment of computational parameters. We have developed a technique for interactive manipulation of simulation parameters by file-based control on an in-situ visualization framework using particle-based volume rendering. We have confirmed that it can be applied to fluid simulation on GPU clusters for interactive visualization and parameter manipulation.
