Comparing Time-to-Solution for In Situ Visualization Paradigms at Scale

This short paper considers time-to-solution for two in situ visualization paradigms: in-line and in-transit. It is a follow-on work to two previous studies. The first study [10] considered time-to-solution (wall clock time) and total cost (total node seconds incurred) for a single visualization algorithm (isosurfacing). The second study [11] considered only total cost and added a second algorithm (volume rendering). This short paper completes the evaluation, considering time-to-solution for both algorithms. In particular, it extends the first study by adding additional insights from including a second algorithm at larger scale and by doing more extended and formal analysis regarding time-to-solution. Further, it complements the second study as the best in situ configuration to choose can vary when considering time-to-solution over cost. It also makes use of the same data corpus used in the second study, although that data corpus has been refactored with time-to-solution in mind.

[1]  Kwan-Liu Ma,et al.  VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures , 2016, IEEE Computer Graphics and Applications.

[2]  James P. Ahrens,et al.  The ALPINE In Situ Infrastructure: Ascending from the Ashes of Strawman , 2017, ISAV@SC.

[3]  Scott Klasky,et al.  In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms , 2016, Comput. Graph. Forum.

[4]  Dmitriy Morozov,et al.  Master of Puppets: Cooperative Multitasking for In Situ Processing , 2016, HPDC.

[5]  Galen M. Shipman,et al.  Accelerating Data Acquisition, Reduction, and Analysis at the Spallation Neutron Source , 2014, 2014 IEEE 10th International Conference on e-Science.

[6]  Scott Klasky,et al.  Opportunities for Cost Savings with In-Transit Visualization , 2020, ISC.

[7]  Hank Childs,et al.  Volume Rendering Via Data-Parallel Primitives , 2015, EGPGV@EuroVis.

[8]  Bianca Prodan,et al.  The Technologies Required for Fusing HPC and Real-Time Data to Support Urgent Computing , 2019, 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[9]  Martin Vejmelka,et al.  An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke , 2019, 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[10]  Robert B. Ross,et al.  Accelerating and Benchmarking Radix-k Image Compositing at Large Scale , 2010, EGPGV@Eurographics.

[11]  Gunther H. Weber,et al.  Performance Analysis, Design Considerations, and Applications of Extreme-Scale In Situ Infrastructures , 2016, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

[12]  Julien Tierny,et al.  Statistical Parameter Selection for Clustering Persistence Diagrams , 2019, 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[13]  Jorge Macías Sánchez,et al.  Urgent Tsunami Computing , 2019, 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC).

[14]  Brian Friesen,et al.  In situ and in-transit analysis of cosmological simulations , 2016, Computational astrophysics and cosmology.

[15]  Michael E. Papka,et al.  Optimal Execution of Co-analysis for Large-Scale Molecular Dynamics Simulations , 2016, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

[16]  Sven Leyffer,et al.  Optimal scheduling of in-situ analysis for large-scale scientific simulations , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[17]  Fan Zhang,et al.  Combining in-situ and in-transit processing to enable extreme-scale scientific analysis , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[18]  Scott Klasky,et al.  Comparing the Efficiency of In Situ Visualization Paradigms at Scale , 2019, ISC.

[19]  Ron A. Oldfield,et al.  Evaluation of methods to integrate analysis into a large-scale shock shock physics code , 2014, ICS '14.

[20]  Stephen A. Jarvis,et al.  CloverLeaf: Preparing Hydrodynamics Codes for Exascale , 2013 .

[21]  Hank Childs,et al.  In Situ Visualization for Computational Science , 2019, IEEE Computer Graphics and Applications.

[22]  Scott Klasky,et al.  DataSpaces: an interaction and coordination framework for coupled simulation workflows , 2012, HPDC '10.

[23]  Arie Shoshani,et al.  Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks , 2014, Concurr. Comput. Pract. Exp..