scholarly journals In-situ visualization for the large scale computing initiative milestone

2019 ◽  
Author(s):  
Jeffrey A. Mauldin ◽  
Thomas J. Otahal ◽  
Anthony M. Agelastos ◽  
Stefan P. Domino
Keyword(s):  
Large Scale ◽  
In Situ Visualization ◽  
Large Scale Computing

In Situ Visualization for Large-Scale Combustion Simulations

2010 ◽  
Vol 30 (3) ◽  
pp. 45-57 ◽  
Author(s):  
Hongfeng Yu ◽  
Chaoli Wang ◽  
Ray W Grout ◽  
Jacqueline H Chen ◽  
Kwan-Liu Ma
Keyword(s):  
Large Scale ◽  
In Situ Visualization

In Situ Visualization and Analysis to Design Large Scale Experiments in Computational Fluid Dynamics

2018 ◽  
Author(s):  
Bennett Bernardoni ◽  
Nicola Ferrier ◽  
Joseph Insley ◽  
Michael E. Papka ◽  
Saumil Patel ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Large Scale ◽  
In Situ Visualization

scholarly journals In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst

2021 ◽  
Author(s):  
Marco Atzori ◽  
Wiebke Köpp ◽  
Steven W. D. Chien ◽  
Daniele Massaro ◽  
Fermín Mallor ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Sets ◽  
High Fidelity Simulation ◽  
Computing Systems ◽  
Performance Simulation ◽  
Image Composition ◽  
In Situ Visualization ◽  
Scale Turbulence

AbstractIn situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only $$\approx 21\%$$ ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is $$\approx 99\%$$ ≈ 99 % ). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.

scholarly journals In-situ visualization library for Yin-Yang grid simulations

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Nobuaki Ohno ◽  
Akira Kageyama
Keyword(s):  
Computer Simulations ◽  
Large Scale ◽  
Visualization Method ◽  
In Situ Visualization ◽  
Yin Yang ◽  
Post Hoc ◽  
Selection Of

AbstractThe visualization of computer simulations is currently undergoing a transition from post-hoc to in-situ visualization in which visualization processes are applied, while the simulation is running. The selection of an appropriate method or tool is essential to efficiently perform in-situ visualization in parallelized large-scale computer simulations that run on supercomputers. Although some generic in-situ visualization libraries are available, they are overengineered for certain geophysical simulations. In this study, we focus on spherical simulations using the Yin-Yang grid. Computer simulations that use the Yin-Yang grid are gaining popularity in geophysics. We propose an in-situ visualization method dedicated to the Yin-Yang grid simulations and demonstrate its effectiveness through sample simulations.

scholarly journals Adaptive In-Situ Visualization Considering Spatio-temporal changes of Large-scale Numerical Simulation Results

2019 ◽  
Vol 2019 (0) ◽  
pp. IS-52
Author(s):  
Yoshiaki YAMAOKA ◽  
Kengo HAYASHI ◽  
Naohisa SAKAMOTO ◽  
Jorji NONAKA ◽  
Tsukasa YOSHINAGA ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Large Scale ◽  
Temporal Changes ◽  
In Situ Visualization ◽  
Simulation Results ◽  
Spatio Temporal

scholarly journals IMPROVING SYSTEM READINESS, STAFF PREPAREDNESS AND PATIENT SAFETY IN A NEW SINGLE FAMILY ROOM NICU THROUGH IN SITU SIMULATION

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e16-e16
Author(s):  
Ahmed Moussa ◽  
Audrey Larone-Juneau ◽  
Laura Fazilleau ◽  
Marie-Eve Rochon ◽  
Justine Giroux ◽  
...  
Keyword(s):  
Patient Safety ◽  
Repeated Measures ◽  
Large Scale ◽  
Multidisciplinary Teams ◽  
Single Family ◽  
In Situ Simulation ◽  
Ensure Patient Safety ◽  
System Readiness ◽  
Dependent Samples

Abstract BACKGROUND Transitions to new healthcare environments can negatively impact patient care and threaten patient safety. Immersive in situ simulation conducted in newly constructed single family room (SFR) Neonatal Intensive Care Units (NICUs) prior to occupancy, has been shown to be effective in testing new environments and identifying latent safety threats (LSTs). These simulations overlay human factors to identify LSTs as new and existing process and systems are implemented in the new environment OBJECTIVES We aimed to demonstrate that large-scale, immersive, in situ simulation prior to the transition to a new SFR NICU improves: 1) systems readiness, 2) staff preparedness, 3) patient safety, 4) staff comfort with simulation, and 5) staff attitude towards culture change. DESIGN/METHODS Multidisciplinary teams of neonatal healthcare providers (HCP) and parents of former NICU patients participated in large-scale, immersive in-situ simulations conducted in the new NICU prior to occupancy. One eighth of the NICU was outfitted with equipment and mannequins and staff performed in their native roles. Multidisciplinary debriefings, which included parents, were conducted immediately after simulations to identify LSTs. Through an iterative process issues were resolved and additional simulations conducted. Debriefings were documented and debriefing transcripts transcribed and LSTs classified using qualitative methods. To assess systems readiness and staff preparedness for transition into the new NICU, HCPs completed surveys prior to transition, post-simulation and post-transition. Systems readiness and staff preparedness were rated on a 5-point Likert scale. Average survey responses were analyzed using dependent samples t-tests and repeated measures ANOVAs. RESULTS One hundred eight HCPs and 24 parents participated in six half-day simulation sessions. A total of 75 LSTs were identified and were categorized into eight themes: 1) work organization, 2) orientation and parent wayfinding, 3) communication devices/systems, 4) nursing and resuscitation equipment, 5) ergonomics, 6) parent comfort; 7) work processes, and 8) interdepartmental interactions. Prior to the transition to the new NICU, 76% of the LSTs were resolved. Survey response rate was 31%, 16%, 7% for baseline, post-simulation and post-move surveys, respectively. System readiness at baseline was 1.3/5,. Post-simulation systems readiness was 3.5/5 (p = 0.0001) and post-transition was 3.9/5 (p = 0.02). Staff preparedness at baseline was 1.4/5. Staff preparedness post-simulation was 3.3/5 (p = 0.006) and post-transition was 3.9/5 (p = 0.03). CONCLUSION Large-scale, immersive in situ simulation is a feasible and effective methodology for identifying LSTs, improving systems readiness and staff preparedness in a new SFR NICU prior to occupancy. However, to optimize patient safety, identified LSTs must be mitigated prior to occupancy. Coordinating large-scale simulations is worth the time and cost investment necessary to optimize systems and ensure patient safety prior to transition to a new SFR NICU.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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