The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The power and energy walls are changing the way users utilize supercomputers: Time-to-completion is not the only important goal but other metrics, such as the energy required to solve a problem or the power efficiency, are becoming as important as performance. This shift towards power- and energy-aware computing is expected to continue in the exascale era, thus, understanding the performance, power...
Prohibitive simulation time with pre-silicon design models and unavailability of proprietary target applications make microprocessor design very tedious. The framework proposed in this paper is the first attempt to automatically generate synthetic benchmark proxies for real world multithreaded applications. The framework includes metrics that characterize the behavior of the workloads in the shared...
Server energy proportionality, as quantified by the proposed EP metric, has improved significantly, from 30-40 percent in 2007 to 50-80 percent today, but much more can be done to move systems closer to ideal.
In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.