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Fast Multi-parameter Performance Modeling

Calotoiu, Alexandru ; Beckingsale, David ; Earl, Christopher W. ; Hoefler, Torsten ; Karlin, Ian ; Schulz, Martin ; Wolf, Felix (2016)
Fast Multi-parameter Performance Modeling.
2016 IEEE International Conference on Cluster Computing (CLUSTER). Taipei, Taiwan (12.-16.09.2016)
doi: 10.1109/CLUSTER.2016.57
Conference or Workshop Item, Bibliographie

Abstract

Tuning large applications requires a clever exploration of the design and configuration space. Especially on supercomputers, this space is so large that its exhaustive traversal via performance experiments becomes too expensive, if not impossible. Manually creating analytical performance models provides insights into optimization opportunities but is extremely laborious if done for applications of realistic size. If we must consider multiple performance-relevant parameters and their possible interactions, a common requirement, this task becomes even more complex. We build on previous work on automatic scalability modeling and significantly extend it to allow insightful modeling of any combination of application execution parameters. Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. We develop a new technique to traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Calotoiu, Alexandru ; Beckingsale, David ; Earl, Christopher W. ; Hoefler, Torsten ; Karlin, Ian ; Schulz, Martin ; Wolf, Felix
Type of entry: Bibliographie
Title: Fast Multi-parameter Performance Modeling
Language: English
Date: 8 December 2016
Publisher: IEEE
Book Title: Proceedings: 2016 IEEE International Conference on Cluster Computing
Event Title: 2016 IEEE International Conference on Cluster Computing (CLUSTER)
Event Location: Taipei, Taiwan
Event Dates: 12.-16.09.2016
DOI: 10.1109/CLUSTER.2016.57
Abstract:

Tuning large applications requires a clever exploration of the design and configuration space. Especially on supercomputers, this space is so large that its exhaustive traversal via performance experiments becomes too expensive, if not impossible. Manually creating analytical performance models provides insights into optimization opportunities but is extremely laborious if done for applications of realistic size. If we must consider multiple performance-relevant parameters and their possible interactions, a common requirement, this task becomes even more complex. We build on previous work on automatic scalability modeling and significantly extend it to allow insightful modeling of any combination of application execution parameters. Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. We develop a new technique to traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning.

Uncontrolled Keywords: BMBF|01IH13001G, DFG|SPPEXA 1648, DoE|DE-SC0015524
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Parallel Programming
Date Deposited: 20 Apr 2018 12:22
Last Modified: 14 May 2024 10:02
PPN: 518255123
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