MaLeFICE: Machine learning support for continuous performance improvement in computational engineering

Name: MaLeFICE: Machine learning support for continuous performance improvement in computational engineering
Author: Sönmezer, Hasan Berk, Muhtaroğlu, Nitel, Arı, İsmail, Gökçin, Deniz

İsim	MaLeFICE: Machine learning support for continuous performance improvement in computational engineering
Yazar	Sönmezer, Hasan Berk, Muhtaroğlu, Nitel, Arı, İsmail, Gökçin, Deniz
Basım Tarihi:	2022-04-25
Basım Yeri	- Wiley
Konu	Batch scheduling, Classification, Cloud, Clustering, DevOp, Docker, Finite element analysis, Machine learning, Virtualization
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1532-0626
Kayıt Numarası	05f37020-5ba4-4d5f-bedb-5b215870e47b
Lokasyon	Computer Science
Tarih	2022-04-25
Örnek Metin	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

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MaLeFICE: Machine learning support for continuous performance improvement in computational engineering

Yazar Sönmezer, Hasan Berk, Muhtaroğlu, Nitel, Arı, İsmail, Gökçin, Deniz

Basım Tarihi 2022-04-25

Basım Yeri - Wiley

Konu Batch scheduling, Classification, Cloud, Clustering, DevOp, Docker, Finite element analysis, Machine learning, Virtualization

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1532-0626

Kayıt Numarası 05f37020-5ba4-4d5f-bedb-5b215870e47b

Lokasyon Computer Science

Tarih 2022-04-25

Örnek Metin 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