HIVE: A Cross-Platform, Modular Visualization Ecosystem for Heterogeneous Computational Environments

Authors: Jorji Nonaka (Riken Center for Computational Science), Kenji Ono (Kyushu University, RIKEN), Naohisa Sakamoto (Kobe University, RIKEN), Kengo Hayashi (Kobe University, RIKEN), Tomohiro Kawanabe (Riken Center for Computational Science), Fumiyoshi Shoji (Riken Center for Computational Science), Masahiro Fujita (LTE Inc), Kentaro Oku (Kashika Inc), Kazuma Hatta (Imagica Digitalscape)

Abstract: HPC operational environments usually have supporting computational systems for assisting pre- and post-processing activities such as the visualization and analysis of simulation results. A wide variety of hardware systems can be found at different HPC sites, and in our case, we have a CPU-only (x86) large memory server, a planned OpenStack-based CPU/GPU Cluster, SPARC64 fx CPU based HPC system (K computer), and an ARM based HPC system in the future. Therefore, heterogeneity and scalability are needed to be tackled to efficiently use these heterogeneous computational resources for large-scale data visualization on both post-hoc and in-situ contexts. In this poster we present HIVE (Heterogeneously Integrated Visual-analytics Environment), a cross-platform and modular ecosystem for providing visualization service building blocks in such heterogeneous computational environments. Lightweight Lua scripting language is used to glue necessary visualization pipeline related modules, and this loosely coupled modular approach facilitates long-term development and maintenance.

Best Poster Finalist (BP): no

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