Author
Muhtaroğlu, Nitel, Arı, İsmail, Kolcu, Birkan
Publication Date
2018-11-10
Publication Place
-
Wiley
Subject
Condition number, Direct solver, Docker, Finite element analysis, Hadoop, HPC-as-a-Service, Iterative solver, Virtual machine
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1532-0626
Record ID
075933ae-b790-4737-8817-0f15263783bc
Library Location
Computer Science
Date
2018-11-10
Notes
IBM Faculty Award ; IBM PhD Fellowship programs ; EU Marie Curie FP7 BI4MASSES project
Sample Text
In this paper, we investigate several design choices for HPC services at different layers of the cloud computing architecture to simplify and broaden its use cases. We start with the platform-as-a-service (PaaS) layer and compare direct and iterative parallel linear equation solvers. We observe that several matrix properties that can be identified before starting long-running solvers can help HPC services automatically select the amount of computing resources per job, such that the job latency is minimized and the overall job throughput is maximized. As a proof of concept, we use classical problems in structural mechanics and mesh these problems with increasing granularities leading to various matrix sizes, ie, largest having 1 billion non-zero elements. In addition to matrix size, we take into account matrix condition numbers, preconditioning effects, and solver types and execute these finite element analysis (FEA) over an IBM HPC cluster. Next, we focus on the infrastructure-as-a-service (IaaS) layer and explore HPC application performance, load isolation, and deployment issues using application containers (Docker) while also comparing them to physical and virtual machines (VM) over a public cloud.
DOI
10.1002/cpe.4782
Cilt
30
