Further enhancing the in situ visualization of performance data in parallel CFD applications

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Abstract

This paper continues the work initiated by the authors on the feasibility of using ParaView as visualization software for the analysis of parallel Computational Fluid Dynamics (CFD) codes’ performance. Current performance tools have limited capacity of displaying their data on top of three-dimensional, framed (i.e., timestepped) representations of the cluster’s topology. In our first paper, a plugin for the open-source performance tool Score-P was 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 were any other flow-related variable. Our second paper added to such plugin the capacity to (also) map communication data (messages exchanged between MPI ranks) to the simulation’s geometry. So far the tool was limited to codes which already have the in situ adapter; but in this paper, we will take the performance data and display it–also in codes without in situ–on a three-dimensional representation of the hardware resources being used by the simulation. Testing is done with the Multi-Grid and Block Tri-diagonal NPBs, as well as Rolls-Royce’s CFD code, Hydra. The benefits and overhead of the plugin’s new functionalities are discussed.

Details

OriginalspracheEnglisch
Aufsatznummere753
Seiten (von - bis)16-31
FachzeitschriftPeerJ computer science
Jahrgang7
PublikationsstatusVeröffentlicht - 25 Okt. 2021
Peer-Review-StatusJa

Externe IDs

researchoutputwizard legacy.publication#88267
WOS 000711164500001
Scopus 85126093907
unpaywall 10.14529/jsfi200402
Mendeley caad1423-d6ff-393c-bf91-d91b17fc3695
unpaywall 10.7717/peerj-cs.753

Schlagworte

Schlagwörter

  • parallel CFD applications, situ visualization of performance data, In situ processing, Parallel computing, Performance analysis

Bibliotheksschlagworte