Abstract In the coming era of exascale supercomputing, in-situ visualization will be a crucial approach for reducing the output data size. A problem of in-situ visualization is that it loses interactivity if a steering method is not adopted. In this paper, we propose a new method for the interactive analysis of in-situ visualization images produced by a batch simulation job. A key idea is to apply numerous (thousands to millions) in-situ visualizations simultaneously. The viewer then analyzes the image database interactively during postprocessing. If each movie can be compressed to 100 MB, one million movies will only require 100 TB, which is smaller than the size of the raw numerical data in exascale supercomputing. We performed a feasibility study using the proposed method. Multiple movie files were produced by a simulation and they were analyzed using a specially designed movie player. The user could change the viewing angle, the visualization method, and the parameters interactively by retrieving an appropriate sequence of images from the movie dataset.
Many in-situ visualizations, with or without steering, have been designed and developed for particular simulation problems. However, general visualization frameworks with high parallel scalability have also become available recently. @cite_7 developed a library that facilitates in-situ visualizations using VisIt, which is one of the most sophisticated parallel visualization tools available today. Their paper also contained a concise review of the history and the latest status of the in-situ visualization research. @cite_1 reported the development of a coprocessing library for , which is another sophisticated parallel visualization tool. Using that library, it is possible to utilize the various visualization functions provided by during runtime, which is decoupled from the simulation. The latest case studies of in-situ visualization using and can be found in @cite_5 .
@cite_5 @cite_1 @cite_7
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As high performance computing approaches exascale, CPU capability far outpaces disk write speed, and in situ visualization becomes an essential part of an analyst's workflow. In this paper, we describe the ParaView Coprocessing Library, a framework for in situ visualization and analysis coprocessing. We describe how coprocessing algorithms (building on many from VTK) can be linked and executed directly from within a scientific simulation or other applications that need visualization and analysis. We also describe how the ParaView Coprocessing Library can write out partially processed, compressed, or extracted data readable by a traditional visualization application for interactive post-processing. Finally, we will demonstrate the library's scalability in a number of real-world scenarios. Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. Yet these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. In this paper, we provide an overview of the Catalyst framework and some of the success stories. In situ visualization is clearly a promising solution for ultrascale simulations. We've seen some success in realizing this solution and in ongoing efforts to add support for in situ visualization to open source visualization toolkits such as ParaView and Visit. However, for others to adopt this approach, we need further research and experimental studies to derive a set of guidelines and usable visualization software components. If this research is successful, it will lead to a new visualization and data-understanding infrastructure, potentially change how scientists work, and accelerate scientific discovery. This paper discusses critical issues in realizing in situ visualization and suggest important research directions. In the context of scientific computing, the computational steering consists in the coupling of numerical simulations with 3D visualization systems through the network. This allows scientists to monitor online the intermediate results of their computations in a more interactive way than the batch mode, and allows them to modify the simulation parameters on-the-fly. While most of existing computational steering environments support parallel simulations, they are often limited to sequential visualization systems. This may lead to an important bottleneck and increased rendering time. To achieve the required performance for online visualization, we have designed the EPSN framework, a computational steering environment that enables to interconnect legacy parallel simulations with parallel visualization systems. For this, we have introduced a redistribution algorithm for unstructured data, that is well adapted to the context of M times N computational steering. Then, we focus on the design of our parallel viewer and present some experimental results obtained with a particle-based simulation in astrophysics