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.
Network analysis software relies on graph layout algorithms to enable users to visually explore network data. Nowadays, networks easily consist of millions of nodes and edges, resulting in hours of computation time to obtain a readable graph layout on a typical workstation. Although these machines usually do not have a very large number of CPU cores, they can easily be equipped with Graphics Processing...
The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide...
Data-parallel programming languages are an important component in today's parallel computing landscape. Among those are domain-specific languages like shading languages in graphics (HLSL, GLSL, RenderMan, etc.) and “general-purpose” languages like CUDA or OpenCL. Current implementations of those languages on CPUs solely rely on multi-threading to implement parallelism and ignore the additional intra-core...
In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms...
Aiming at the low efficiency when traditional realization methods of map algebra apply to calculations for gigantic raster data, this paper maps the traditional serial algorithms to GPU parallel processing architecture on a new parallel programming model of GPU named Compute Unified Device Architecture. The paper also aims to discuss the realization mechanism surrounding parallel mapping methods from...
There is a multicore platform that is currently concentrating an enormous attention due to its tremendous potential in terms of sustained performance: the NVIDIA Tesla boards. These cards intended for general-purpose computing on graphic processing units (GPGPUs) are used as data-parallel computing devices. They are based on the Computed Unified Device Architecture (CUDA) which is common to the latest...
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.