In-situ workflow auto-tuning through combining component models
Document Type
Conference Proceeding
Publication Date
2-17-2021
Abstract
In-situ parallel workflows couple multiple component applications via streaming data transfer to avoid data exchange via shared file systems. Such workflows are challenging to configure for optimal performance due to the huge space of possible configurations. Here, we propose an in-situ workflow auto-tuning method, ALIC, which integrates machine learning techniques with knowledge of in-situ workflow structures to enable automated workflow configuration with a limited number of performance measurements. Experiments with real applications show that ALIC identify better configurations than existing methods given a computer time budget.
Identifier
85101706149 (Scopus)
ISBN
[9781450382946]
Publication Title
Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming PPOPP
External Full Text Location
https://doi.org/10.1145/3437801.3441615
First Page
467
Last Page
468
Fund Ref
U.S. Department of Energy
Recommended Citation
Shu, Tong; Guo, Yanfei; Wozniak, Justin; Ding, Xiaoning; Foster, Ian; and Kurc, Tahsin, "In-situ workflow auto-tuning through combining component models" (2021). Faculty Publications. 4317.
https://digitalcommons.njit.edu/fac_pubs/4317
