Private IaaS clouds are an attractive environment for scientific workloads and applications. It provides advantages such as almost instantaneous availability of high-performance computing in a single node as well as c...
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ISBN:
(纸本)9781538678800
Private IaaS clouds are an attractive environment for scientific workloads and applications. It provides advantages such as almost instantaneous availability of high-performance computing in a single node as well as compute clusters, easy access for researchers, and users that do not have access to conventional supercomputers. Furthermore, a cloud infrastructure provides elasticity and scalability to ensure and manage any software dependency on the system with no third-party dependency for researchers. However, one of the biggest challenges is to avoid significant performance degradation when migrating these applications from physical nodes to a cloud environment. Also, we lack more research investigations for multi-tenant cloud instances. In this paper, our goal is to perform a comparative performance evaluation of scientific applications with single and multi-tenancy cloud instances using KVM and LXC virtualization technologies under private cloud conditions. All analyses and evaluations were carried out based on NAS Benchmark kernels to simulate different types of workloads. We applied statistic significance tests to highlight the differences. The results have shown that applications running on LXC-based cloud instances outperform KVM-based cloud instances in 93.75% of the experiments w.r.t single tenant. Regarding multi-tenant, LXC instances outperform KVM instances in 45% of the results, where the performance differences were not as significant as expected.
Mathematical optimization algorithms are ubiquitous in computational science and engineering where the objective function of the optimization problem involves a complicated computer model predicting relevant phenomena...
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Mathematical optimization algorithms are ubiquitous in computational science and engineering where the objective function of the optimization problem involves a complicated computer model predicting relevant phenomena of a scientific or engineering system of interest. Therefore, in this area of mathematical software, it is indispensable to combine software for optimization with software for simulation, typically developed independently of each other by members of separate scientific communities. From a software engineering point of view, the situation becomes even more challenging when the simulation software is developed using a parallel programming paradigm without taking into consideration that it will be executed within an optimization context. The EFCOSS environment alleviates some of the problems by serving as an interfacing layer between optimization software and simulation software. In this paper, we show the software design of those parts of EFCOSS that are relevant to the integration of a simulation software involving different parallel programming paradigms. The parallel programming paradigms supported by EFCOSS include MPI for distributed memory and OpenMP for shared memory. In addition, the simulation software can be executed on a remote parallel computer.
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