Further enhancing the in situ visualization of performance data in parallel CFD applications
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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
Original language | English |
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Article number | e753 |
Pages (from-to) | 16-31 |
Journal | PeerJ computer science |
Volume | 7 |
Publication status | Published - 25 Oct 2021 |
Peer-reviewed | Yes |
External IDs
researchoutputwizard | legacy.publication#88267 |
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WOS | 000711164500001 |
Scopus | 85126093907 |
unpaywall | 10.14529/jsfi200402 |
Mendeley | caad1423-d6ff-393c-bf91-d91b17fc3695 |
unpaywall | 10.7717/peerj-cs.753 |
Keywords
Keywords
- parallel CFD applications, situ visualization of performance data, In situ processing, Parallel computing, Performance analysis