Abstract
Supercomputers are being widely used for applications that require high speed computing, such as weather forecasting, spaceship and aircraft design and simulation, and analysis of geological and seismic data, to name a few. These machines run multiprogrammed time-sharing operating systems, so that their facilities can be shared by many local and remote users.Therefore, it is important to be able to assess the performance of supercomputers in multiprogrammed environments. Most studies of supercomputers performance are concerned sith the evaluation of the effective speed of a program running in isolation on a particular supercomputer. Analytic models based on Queueing Networks (QNs) and Stochastic Petri Nets (SPNs) are used in this paper with two purposes. The first is to evaluate the performance of supercomputers in multiprogrammed environments, and the second is to compare performance-wise conventional supercomputer architectures with a novel architecture proposed here. It is shown, with the aid of the analytic models, that the proposed architecture is preferable performance-wise over the existing conventional supercomputer architectures. A three level workload characterization model for supercomputers is presented.Input data for the numerical examples discussed here are extracted from the well known Los Alamos Benchmark.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
