February 27, 2013
Everybody loves predictions. Here's a few made by IEEE group members at SC12 in case you missed it. Read more…
February 14, 2013
If you have an office betting pool about which college students are likely to win the next cluster challenge, here are some things you should know. Read more…
December 13, 2012
In the world of high performance computing, a lot of the important news this year revolved around heterogeneous computing, big data, and HPC interconnects. Two vendors that perhaps embodied those technologies more so than others were Cray and Intel, both of which figured prominently in some of the biggest HPC stories in 2012. Here are HPCwire's highlights and lowlights for the year. Read more…
November 27, 2012
At SC12, Adaptive announced its Moab HPC Suite 7.2 release, which includes several productivity enhancements and introduces support for Intel Xeon Phi coprocessors. The workload management vendor also launched two new products as part of its Moab HPC Suite: Application Portal Edition and Remote Visualization Edition. Read more…
November 16, 2012
NCSA's Blue Waters system is one of the fastest supercomputers in the world, but it won't be appearing on the TOP500 list, nor will it be taking part in the HPC Challenge awards. HPCwire spoke with Project Director Bill Kramer to get the full story on this important decision. Read more…
November 16, 2012
Imagine a world where a computer chip costs just a penny. They could be embedded anywhere and everywhere. Celebrity physicist Michio Kaku talked about the implications of this and much more in his much-anticipated keynote address at Supercomputing 2012 (SC12) this week in Salt Lake City, where he discussed the huge role that high performance computing will play in the year 2100. Read more…
November 16, 2012
In a part serious, part provocative style, here is a light-hearted look at the different personality stereotypes involved in high performance computing. This is by no means an exclusive list, but it does illustrate the range of people who contribute to the flavor of the world of supercomputing. Read more…
November 14, 2012
PLX Technology, a maker of PCIe switches and bridges, has been spreading the word about using PCIe as a general-purpose HPC interconnect. At SC12 this week, the company went a little further down that path, unveiling their upcoming Express Fabric technology. We asked Vijay Meduri, executive vice president of engineering at PLX to talk about the rationale of using PCIe as an interconnect fabric. Read more…
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.
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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