Author
Sönmezer, Hasan Berk, Muhtaroğlu, Nitel, Arı, İsmail, Gökçin, Deniz
Publication Date
2022-04-25
Publication Place
-
Wiley
Subject
Batch scheduling, Classification, Cloud, Clustering, DevOp, Docker, Finite element analysis, Machine learning, Virtualization
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1532-0626
Record ID
05f37020-5ba4-4d5f-bedb-5b215870e47b
Library Location
Computer Science
Date
2022-04-25
Sample Text
Computer aided engineering (CAE) practices improved drastically within the last decade due to ease of access to computing resources and open-source software. However, increasing complexity of hardware and software settings and the scarcity of multiskilled personnel rendered the practice inefficient and infeasible again. In this article, we present a method for continuous performance improvement in computational engineering that combines online performance profiling with machine learning (ML). To test the viability of this method, we provide a detailed analysis for solution time estimation of finite element analysis (FEA) jobs based on multidimensional models. These models combine numerous matrix features (matrix size, density, bandwidth, etc.), solver features (direct-iterative, preconditioning, tolerance), and hardware features (core count, virtual–physical). We repeat our analysis over different machines as well as docker containers to demonstrate applicability over different platforms. Next, we train supervised and unsupervised ML algorithms over commonly used, realistic FEA benchmarks and compare accuracy of different models. Finally, we design two new ML-based online batch schedulers called shortest predicted time first (SPTF) and shortest cluster time first (SCTF), which are comparable in performance to the optimal, but offline shortest job first (SJF) scheduler. We find that ML-based profiling and scheduling can reduce the average turnaround times by 2x –5x over other alternatives.
DOI
10.1002/cpe.6674
Cilt
34
