Supporting Runtime Tool Interaction for Parallel Simulations

Harrop, Christopher W.; Hackstadt, Steven T.; Cuny, Janice E.; Malony, Allen D.; Magde, Laura S.

doi:10.1109/sc.1998.10009

Cited by 3 publications

(2 citation statements)

References 10 publications

(8 reference statements)

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“…Their primarily focus was on issues including memory efficiency, long running jobs, extensibility, and building steerable applications from existing code. Harrop et al [5] presented a tool called TierraLab that is built on top of a MATLAB interface and extends the geoscientists' existing tomography code with runtime interaction capabilities. Modi et al [13] introduced a new, light-weight steering system based on a client/server programming model and demonstrated the effectiveness of the system on a real-time wake-vortex simulations for multiple aircraft running on a parallel Beowulf cluster.…”

Section: Related Workmentioning

confidence: 99%

In-situ processing and visualization for ultrascale simulations

Wang

et al. 2007

J. Phys.: Conf. Ser.

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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.

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Section: Related Workmentioning

confidence: 99%

In-situ processing and visualization for ultrascale simulations

Wang

et al. 2007

J. Phys.: Conf. Ser.

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“…Some well-known examples are the on-line monitoring and steering environment Falcon [5], the Computational Steering Environment Cse [6], the Visualization and Steering Environment Vase [1], the runtime interaction framework Daqv [8] and its successor Daqv-II [9], the visualization and steering library Cumulvs [7], and the scientific programming environment Scirun [16].…”

Section: Introductionmentioning

confidence: 99%

The Monitoring and Steering Environment

Glasner

Hügl

Reitinger

et al. 2001

Computational Science - ICCS 2001

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Abstract. Monitoring and steering promises interactive computing and visualization for large-scale scientific simulations. This is achieved by retrieving data about a program's execution during runtime, analyzing these data with sophisticated graphical representations, and applying changes to parameters of the simulation in progress. Based on these requirements the MoSt environment offers some novel ideas: Firstly, instrumentation of the code is performed on-the-fly during execution without a priori preparation of the target application's code. Secondly, all functionality is provided in modules which communicate with each other via a specialized network protocol, allowing the user to adapt MoSt to specific situations. Thirdly, visual representations can be generated by arbitrary visualization packages, which integrate an interface to the monitoring environment. The latter may also apply Virtual Reality techniques in order to enhance the user's understanding. This paper offers an overview of MoSt, the strategy behind it, and its main modules.

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Interactive steering on in situ particle-based volume rendering framework

Kawamura,

Hasegawa,

Idomura

2023

J Vis

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The development of supercomputers and multi-scale computational fluid dynamics (CFD) models based on adaptive mesh refinement (AMR) enabled fast, large-scale, and high fidelity CFD simulations. Interactive in situ steering is an effective tool for debugging, searching for optimal solutions, and analyzing inverse problems in such CFD simulations. We propose an interactive in situ steering framework for large-scale CFD simulations on GPU supercomputers. This framework employs in situ particle-based volume rendering (PBVR), in situ data sampling, and a file-based control that enables interactive and asynchronous communication of steering parameters, compressed visualization particle data, and sampled monitoring data between supercomputers and user PCs. The parallelized PBVR is processed on the host CPU to avoid interference with CFD simulations on the GPU. We apply the proposed framework to a real-time plume dispersion analysis code CityLBM, which computes the lattice Boltzmann method on the block AMR grid using GPU supercomputers. In the numerical experiment, we address an inverse problem to find a pollutant source from the observation data at monitoring points and demonstrate the effectiveness of the human-in-the-loop approach via the in situ steering framework. Graphical abstract

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Supporting Runtime Tool Interaction for Parallel Simulations

Cited by 3 publications

References 10 publications

In-situ processing and visualization for ultrascale simulations

In-situ processing and visualization for ultrascale simulations

The Monitoring and Steering Environment

Interactive steering on in situ particle-based volume rendering framework

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