February 17, 2017
Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…
February 16, 2017
In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…
May 5, 2011
The Weekly Top Five features the five biggest HPC stories of the week, condensed for your reading pleasure. This week, we cover ISRO's newest supercomputer; Tokyo Tech's selection of EM Photonics' CULA library; Intel's 3-D transistor breakthrough; the latest LSF Tools from Platform Computing; and SciNet's new NextIO GPU-based system. Read more…
October 14, 2010
When the TSUBAME 2.0 supercomputer is formally inaugurated in December, it will officially be declared the fastest supercomputer in Japan. However, it’s not simply speed that separates this machine; boasting a raw performance of 2.4 petaflops, the new TSUBAME exceeds the total FLOPS capacity of all other government and academic supercomputers in Japan today. That kind of computational brawn will make it the platform of choice for some of the most powerful scientific applications on the planet. Read more…
June 24, 2010
Tokyo Tech releases more details on Tsumabe 2.0 in anticipation of fall startup; and several Web-centric servers make their debut. We recap those stories and more in our weekly wrapup. Read more…
April 1, 2010
Cray wins NNSA supercomputing contract; and Tokyo Tech researchers make breakthroughs in weather forecasting using GPU computing. We recap those stories and more in our weekly wrapup. Read more…
November 18, 2009
Buying Teslas by the bushel. Read more…
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.
Karlsruhe Institute of Technology (KIT) is an elite public research university located in Karlsruhe, Germany and is engaged in a broad range of disciplines in natural sciences, engineering, economics, humanities, and social sciences. For institutions like KIT, HPC has become indispensable to cutting-edge research in these areas.
KIT’s HoreKa supercomputer supports hundreds of research initiatives including a project aimed at predicting when the Earth’s ozone layer will be fully healed. With HoreKa, projects like these can process larger amounts of data enabling researchers to deepen their understanding of highly complex natural processes.
Read this case study to learn how KIT implemented their supercomputer powered by Lenovo ThinkSystem servers, featuring Lenovo Neptune™ liquid cooling technology, to attain higher performance while reducing power consumption.
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