Browsing by Author "Kolcu, Birkan"

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    Democratization of HPC cloud services with automated parallel solvers and application containers
    (Wiley, 2018-11-10) Muhtaroğlu, Nitel; Arı, İsmail; Kolcu, Birkan; Computer Science; ARI, Ismail; Kolcu, Birkan; Muhtaroğlu, Nitel
    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.
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    Testing performance of application containers in the cloud with hpc loads
    (Civil-Comp, 2017) Muhtaroglu, Nitel; Kolcu, Birkan; Arı, İsmail; Computer Science; Iványi, P.; Topping, B.H.V.; Várady, G.; ARI, Ismail; Muhtaroglu, Nitel; Kolcu, Birkan
    In this paper, we evaluate HPC application deployment using container technology and examine performance metrics on a public cloud. The research focuses on the ease of deployment, performance and isolation issues related to application containers (Docker) and HPC software, which is OpenFOAM in our case. We find that performance of Docker is comparable to Physical or Virtual HPC alternatives with negligible overheads (5%), whereas its savings in setup and deployment times are drastically better (10-15x).