Enhancing the in Situ Visualization of Performance Data in Parallel CFD Applications

Rigel F. C. Alves; Andreas Knüpfer

doi:10.14529/jsfi200402

Authors

Rigel F. C. Alves https://tu-dresden.de/
Andreas Knüpfer https://tu-dresden.de/

DOI:

https://doi.org/10.14529/jsfi200402

Abstract

This paper continues the work initiated by the authors on the feasibility of using ParaView as visualization software for the analysis of parallel CFD codes’ performance. Current performance tools are unable to show their data on top of complex simulation geometries (e.g. an aircraft engine). In our previous paper, a plugin for the open-source performance tool Score-P has been introduced, which intercepts an arbitrary number of manually selected code regions (mostly functions) and send their respective measurements – amount of executions and cumulative time spent – to ParaView (through its in situ library, Catalyst), as if they are any other flow-related variable. This paper adds to such plugin the capacity to also show communication data (messages sent between MPI ranks) on top of the CFD mesh. Testing is done again with Rolls-Royce’s in-house CFD code, Hydra. The plugin’s original feature (regions’ measurements) is here revisited, in a bigger test-case, which is also used to illustrate the new feature (communication data). The benefits and overhead of the tool are discussed.

References

Ahrens, J., Geveci, B., Law, C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005)

Alves, R.F.C., Knüpfer, A.: In situ visualization of performance-related data in parallel CFD applications. In: Schwardmann, U., Boehme, C., B. Heras, D., et al. (eds.) Euro-Par 2019: Parallel Processing Workshops. pp. 400–412. Springer International Publishing, Cham (2020), DOI: 10.1007/978-3-030-48340-1 31

Ayachit, U., Bauer, A., Geveci, B., et al.: Paraview Catalyst: Enabling in situ data analysis and visualization. In: Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, ISAV2015, Austin, TX, USA. pp. 25–29. ACM (2015), DOI: 10.1145/2828612.2828624

Bremer, P.T., Mohr, B., Pascucci, V., et al.: Connecting Performance Analysis and Visualization. Dagstuhl Manifestos 5(1), 1–24 (2015), DOI: 10.4230/DagMan.5.1.1

Huck, K.A., Potter, K., Jacobsen, D.W., et al.: Linking performance data into scientific visualization tools. In: 2014 First Workshop on Visual Performance Analysis. pp. 50–57. IEEE (2014), DOI: 10.1109/VPA.2014.9

Husain, B., Giménez, A., Levine, J.A., et al.: Relating memory performance data to application domain data using an integration API. In: Proceedings of the 2nd Workshop on Visual Performance Analysis, VPA ’15, Austin, Texas. ACM (2015), DOI: 10.1145/2835238.2835243

Isaacs, K.E., Giménez, A., Jusufi, I., et al.: State of the Art of Performance Visualization. In: Borgo, R., Maciejewski, R., Viola, I. (eds.) EuroVis - STARs. The Eurographics Association (2014), DOI: 10.2312/eurovisstar.20141177

Khanal, B., He, L., Northall, J., et al.: Analysis of Radial Migration of Hot-Streak in Swirling Flow Through High-Pressure Turbine Stage. Journal of Turbomachinery 135(4) (2013), DOI: 10.1115/1.4007505

Knüpfer, A., Rössel, C., Mey, D.a., et al.: Score-P: A joint performance measurement runtime infrastructure for Periscope, Scalasca, TAU, and Vampir. In: Brunst, H., Müller, M.S., Nagel, W.E., et al. (eds.) Tools for High Performance Computing 2011. pp. 79–91. Springer Berlin Heidelberg, Berlin, Heidelberg (2012), DOI: 10.1007/978-3-642-31476-6_7

Lapworth, L.: Hydra-CFD: a framework for collaborative CFD development. In: International Conference on Scientific and Engineering Computation, IC-SEC, June, Singapore. vol. 30 (2004), https://www.researchgate.net/publication/316171819_HYDRA-CFD_A_Framework_for_Collaborative_CFD_Development

Schöne, R., Tschüter, R., Ilsche, T., et al.: Extending the functionality of Score-P through plugins: Interfaces and use cases. In: Niethammer, C., Gracia, J., Hilbrich, T., et al. (eds.) Tools for High Performance Computing 2016. pp. 59–82. Springer International Publishing, Cham (2017), DOI: 10.1007/978-3-319-56702-0_4

Vierjahn, T., Kuhlen, T.W., Müller, M.S., et al.: Visualizing performance data with respect to the simulated geometry. In: Di Napoli, E., Hermanns, M.A., Iliev, H., et al. (eds.) High-Performance Scientific Computing. pp. 212–222. Springer International Publishing, Cham (2017), DOI: 10.1007/978-3-319-53862-4_18

Wood, C., Larsen, M., Gimenez, A., et al.: Projecting performance data over simulation geometry using SOSflow and ALPINE. In: Bhatele, A., Boehme, D., Levine, J.A., et al. (eds.) Programming and Performance Visualization Tools. pp. 201–218. Springer International Publishing, Cham (2019), DOI: 10.1007/978-3-030-17872-7_12