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.
Nowadays Graphic Processing Units (GPU) are gaining increasing popularity in high performance computing (HPC). While modern GPUs can offer much more computational power than CPUs, they also consume much more power. Energy efficiency is one of the most important factors that will affect a broader adoption of GPUs in HPC. In this paper, we systematically characterize the power and energy efficiency...
Stencil computation sweeps over a spatial grid over multiple time steps to perform nearest-neighbor computations. The bandwidth-to-compute requirement for a large class of stencil kernels is very high, and their performance is bound by the available memory bandwidth. Since memory bandwidth grows slower than compute, the performance of stencil kernels will not scale with increasing compute density...
This paper presents an approximate belief propagation algorithm that replaces outgoing messages from a node with the averaged outgoing message and propagates messages from a low resolution graph to the original graph hierarchically. The proposed method reduces the computational time by half or two-thirds and reduces the required amount of memory by 60% compared with the standard belief propagation...
We are using GPUs to run a new weather model being developed at NOAA's Earth System Research Laboratory (ESRL). The parallelization approach is to run the entire model on the GPU and only rely on the CPU for model initialization, I/O, and inter-processor communications. We have written a compiler to convert Fortran into CUDA, and used it to parallelize the dynamics portion of the model. Dynamics,...
Recently, Computer Vision problems like Face Recognition and Super-Resolution solved using sparse representation based methods with large dictionaries have shown state-of-the-art results. However such methods are computationally prohibitive for typical CPUs, especially for a large dictionary size. We present fast implementation of these methods by exploiting the massively parallel processing capabilities...
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.