September 21, 2012
Since its first announcement on June 28, and its official start on July 20, the Uber-Cloud Experiment has attracted over 160 industry and research organizations and individuals from 22 countries. They all share one goal: to jointly explore the end-to-end process of remotely accessing technical computing resources sitting in HPC centers and in the cloud. The focus of this experiment is on engineering simulations performed by small and medium enterprises that expect a quantum leap in innovation and competitiveness by using HPC.
The benefits of remote access to HPC are widely recognized. We have technology that allows us to access and run engineering workloads on remote resources, for example, but we still face other challenges related to the human element. Examples of this include trusting the resource provider; giving away some control over our applications, data, and resources; security; provider lock-in; software licensing; unfamiliar pay-per-use computing models; and a general lack of clarity in distinguishing between hype and reality. To explore these hurdles in detail and to learn more about this end-to-end process, we were able to build the following 20 teams: Anchor Bolt, Resonance, Radiofrequency, Supersonic, Liquid-Gas, Wing-Flow, Ship-Hull, Cement-Flows, Sprinkler, Space Capsule, Car Acoustics, Dosimetry, Weathermen, Wind Turbine, Combustion, Blood Flow, ChinaCFD, Gas Bubbles, Side impact, and ColombiaBio.
During the half-time webinar we asked the attendees if they wished to participate in a second round of the Uber-Cloud Experiment, and 97% answered in the affirmative. Therefore, we decided to start a new round of the Uber-Cloud Experiment right after the end of the current round, running from mid-November to mid-February.
The full report, available on HPCwire, provides additional information about the different teams, two engineering use cases, and further details on Round 2. Registration for Round 2 can be accessed from the Uber-Cloud Experiment website.
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.