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.
In-situ visualization has a long history. @cite_8 conducted a general review of steering simulation and visualization. Examples of in-situ visualization in peta-scale simulations and their technical challenges (especially those caused by massively parallel processing) are summarized in @cite_3 @cite_2 . They show that in-situ visualization is a promising solution for peta and exascale simulations. A natural extension of the online dynamical control of simulation is the end-to-end approach @cite_12 @cite_10 , where even mesh generation, which is usually conducted in the preprocessing stage, is performed on supercomputers. They developed a steering simulation for seismic wave propagation where the visualization images were shown in real time.
@cite_8 @cite_3 @cite_2 @cite_10 @cite_12
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Most researchers who perform data analysis and visualization do so only after everything else is finished, which often means that they don't discover errors invalidating the results of their simulation until post-processing. A better approach would be to improve the integration of simulation and visualization into the entire process so that they can make adjustments along the way. This approach, called computational steering, is the capacity to control all aspects of the computational science pipeline. Recently, several tools and environments for computational steering have begun to emerge. These tools range from those that modify an application's performance characteristics (either by automated means or by user interaction) to those that modify the underlying computational application. A refined problem-solving environment should facilitate everything from algorithm development to application steering. The authors discuss some tools that provide a mechanism to integrate modeling, simulation, data analysis and visualization. The growing power of parallel supercomputers gives scientists the ability to simulate more complex problems at higher fidelity, leading to many high-impact scientific advances. To maximize the utilization of the vast amount of data generated by these simulations, scientists also need scalable solutions for studying their data to different extents and at different abstraction levels. As we move into peta- and exa-scale computing, simply dumping as much raw simulation data as the storage capacity allows for post-processing analysis and visualization is no longer a viable approach. A common practice is to use a separate parallel computer to prepare data for subsequent analysis and visualization. A naive realization of this strategy not only limits the amount of data that can be saved, but also turns I O into a performance bottleneck when using a large parallel system. We conjecture that the most plausible solution for the peta- and exa-scale data problem is to reduce or transform the data in-situ as it is being generated, so the amount of data that must be transferred over the network is kept to a minimum. In this paper, we discuss different approaches to in-situ processing and visualization as well as the results of our preliminary study using large-scale simulation codes on massively parallel supercomputers. 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. Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline - problem description and interpretation of the output - have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components - meshing, partitioning, solver, and visualization - are tightly coupled and execute in parallel with shared data structures and no intermediate I O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-to-end approach to overcome the scalability bottlenecks of the traditional approach