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Proving the Case for Climate Change with Hi-Res Models
August 2, 2012
Although serious scientists believe we’re past the point of debating the validity of climate change, the computer models that support this research are not perfect. Fortunately, the latest improvements to high-resolution climate simulations are not only improving the fidelity of the models, but are also deepening our understanding of climate dynamics, both qualitatively and quantitatively. Read more…
XSEDE Gaining Speed as Year Two Begins
July 25, 2012
Project directory John Towns outlines new projects, cites first-year successes. Read more…
NSF’s Seidel: ‘Software is the Modern Language of Science’
August 9, 2011
Edward Seidel, the former director of the NSF's Office of Cyberinfrastructure, told attendees at TeraGrid ’11 that after more than four centuries of science being conducted at a painstakingly slow pace, today’s communications technologies and scientific advances are forcing a dramatic change -- and acceleration -- in all areas of science. At the heart of this change will be software. Read more…
Life After TeraGrid
July 28, 2011
XSEDE takes up NSF's cyberinfrastructure mission with a broader mandate. Read more…
Supercomputing for the Birds
July 11, 2011
A recent ornithology research endeavor harnessed the Teragrid for 70,000 hours of compute time. Read more…
MATLAB on the TeraGrid One Year Later
October 20, 2010
The MATLAB on the TeraGrid initiative, deployed last year at SC09, is still going strong. Read more…
Enabling Research with MATLAB on the TeraGrid
October 18, 2010
Last year Cornell University and Purdue University received funding from the National Science Foundation to undertake their MATLAB on the TeraGrid project. Since its inception a number of researchers have been making use of the resource and Cornell's Center for Advanced Computing is demonstrating that the resource might have a permanent place in the TeraGrid resource provider collection in the future. Read more…
TeraGrid 2010 Conference Campus Champion Program
August 18, 2010
Fifty-one Campus Champions attended TeraGrid's fifth annual conference in Pittsburgh, Pa., recently. Accounting for about 15 percent of attendees, the champions bring a wealth of experience to the program, including knowledge of TeraGrid systems and services, their local campus resources, and other cyberinfrastructures such as the Open Science Grid. This is our fifth and final in a series covering the TeraGrid conference. Read more…
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Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
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How to Save 80% with TotalCAE Managed On-prem Clusters and Cloud
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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