July 9, 2013
The global HPC market has recovered from the great recession and is now poised to grow at a compound annual growth rate (CAGR) of 6.8 to 7.8 percent through 2016, with growth at the high end supercomputer market and China leading the way, the research firm IDC said at the recent ISC 2013 event in Germany. Read more…
June 27, 2013
When an underdog team of undergrads from South Africa arrived in Leipzig, Germany for the 2013 Student Cluster Challenge last week, they had the odds stacked against them. But what the team lacked in experience was more than made up for with intestinal fortitude, not to mention a heavy dose of NVIDIA GPUs. Read more…
June 24, 2013
While there is a universal desire in the HPC community build the world's exascale system, the achievement will require a major breakthrough in not only chip design and power utilization but programming methods, NVIDIA chief scientist Bill Dally said in a keynote address at ISC 2013 last week in Leipzig, Germany. Read more…
June 21, 2013
To wrap up ISC, we wanted to collect some general visualized trends from this year's rankings. In this visual feature, we've provided information on operating systems, key vendors, processor and interconnect technologies and more. While there are a million ways to analyze the... Read more…
June 20, 2013
China is making significant strides towards its goals to use supercomputing as the next big platform to spur economic growth. The country now makes up 13% of the Top 500 (with 51% from the United States). We talk with Inspur's Chief Scientist of HPC Applications about how the system will be used to forward the goals of the national... Read more…
June 20, 2013
Worldwide sales of HPC servers perked up by 5.3 percent during the first quarter of 2013, to $2.5 billion, industry watchers at IDC reported last week. The increase was driven by sales of small and midrange HPC systems, as sales of high-end supercomputers declined. Read more…
June 20, 2013
During a conversation this week with Cray CEO, Peter Ungaro, we learned that the company has managed to extend its reach into the enterprise HPC market quite dramatically--at least in supercomputing business terms. With steady growth into these markets, however, the focus on hardware versus the software side of certain problems for such users is.... Read more…
June 18, 2013
Contributing commentator, Andrew Jones, offers a break in the news cycle with an assessment of what the national "size matters" contest means for the U.S. and other nations... 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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