Paper Info
Title
In-situ workflow auto-tuning through combining component models
Abstract
ABSTRACTIn-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.
Year
DOI
Venue
2021
10.1145/3437801.3441615
Principles and Practice of Parallel Programming
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Tong Shu101.01
Yanfei Guo211.37
Justin M. Wozniak346435.32
Xiaoning Ding401.35
Foster Ian5229382663.24
Tahsin M. Kurç61423149.77