Democratization of HPC cloud services with automated parallel solvers and application containers

Name: Democratization of HPC cloud services with automated parallel solvers and application containers
Author: Muhtaroğlu, Nitel, Arı, İsmail, Kolcu, Birkan

İsim	Democratization of HPC cloud services with automated parallel solvers and application containers
Yazar	Muhtaroğlu, Nitel, Arı, İsmail, Kolcu, Birkan
Basım Tarihi:	2018-11-10
Basım Yeri	- Wiley
Konu	Condition number, Direct solver, Docker, Finite element analysis, Hadoop, HPC-as-a-Service, Iterative solver, Virtual machine
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1532-0626
Kayıt Numarası	075933ae-b790-4737-8817-0f15263783bc
Lokasyon	Computer Science
Tarih	2018-11-10
Notlar	IBM Faculty Award ; IBM PhD Fellowship programs ; EU Marie Curie FP7 BI4MASSES project
Örnek Metin	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

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Democratization of HPC cloud services with automated parallel solvers and application containers

Yazar Muhtaroğlu, Nitel, Arı, İsmail, Kolcu, Birkan

Basım Tarihi 2018-11-10

Basım Yeri - Wiley

Konu Condition number, Direct solver, Docker, Finite element analysis, Hadoop, HPC-as-a-Service, Iterative solver, Virtual machine

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1532-0626

Kayıt Numarası 075933ae-b790-4737-8817-0f15263783bc

Lokasyon Computer Science

Tarih 2018-11-10

Notlar IBM Faculty Award ; IBM PhD Fellowship programs ; EU Marie Curie FP7 BI4MASSES project

Örnek Metin 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