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.
Energy efficiency of GPUs has facilitated the usage of GPUs in many complex scientific applications. Nodes with multi-GPUs along with multi-core CPUs are quite common in today's HPC landscape. This gives the flexibility to utilize CPUs or accelerators or even both according to the workload characteristics. It is not possible to measure power and energy accurately in all the cases, an alternate approach...
To improve the energy efficiency of parallel ap- plications on GPGPUs, a better understanding of the energy behavior of various applications is mandatory. In this study we employ statis- tical methods to model power and energy con- sumption of some common optimized high per- formance kernels (DGEMM, FFT, PRNG and FD stencils) on a multi-GPU platform.
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.