September 02, 2010
Researchers examining how tornadoes form have turned into “twister chasers” rushing from site to site to get their equipment on the ground before the tornado hits. Their efforts are aimed at more accurately predicting when touchdown will occur to improve evacuation and warning, but this is, as one can imagine, quite a dangerous and troublesome process. Simply getting to the cell’s site at the right time is a challenge for the VORTEX2 group, thus other compute-based esearchers are looking to distributed computing to take on the challenge and reduce the complex of fieldwork.
The Center for Analysis and Prediction of Storms undertook a hybrid computing project called the “Linked Environments for Atmospheric Discovery II” (LEAD II) with the help of the Big Red cluster and Microsoft’s Azure to better predict tornado conditions and to aid in the advancement of VORTEX2 goals.
Although LEAD II is only a fraction of the larger VORTEX2 project, part of what makes it remarkable is that it signals the first use of a hybrid workflow model for the participants, which was created using Microsoft’s Trident Scientific Workflow Workbench to handle the front-end workflow system, which then doled out pieces of it to backend Unix and Linux-based resources, including Indiana University’s Big Red.
As LEAD II researcher Beth Plale explained, “A lot of scientists use Windows tools such as Excel…We think that utilizing a Windows workflow system on a Windows box is a step towards providing broader flexibility, because of this affinity of a lot of scientists to use Excel and because of the emergence of the cloud-based Azure platform.”
Overall, the team decided that the use of the hybrid workflow model for the project was a success due to the added flexibility it offered, not to mention a great exercise in experimenting with different models for use in future projects.
Full story at International Science Grid This Week
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.