Back at the International Supercomputing Conference in June, supercomputer maker Eurotech dropped some hints about its future water-cooled Aurora systems that would employ a mix of ARM processors …
Huge cheers broke out in the New Orleans Theater inside the Ernest N. Morial Convention Center today when Team Longhorn from the University of Texas at Austin was declared the overall winner of …
In celebration of SC14, we’ve decided to put together a daily list of some the top tweets from the event. For those unable to attend, we hope this gives you a window into the happenings of SC14 …
December 4, 2014
One of the highlights of SC14 was a focus on how HPC is expanding out of its roots and cropping up in more and more places. One of the more interesting use case Read more…
November 19, 2014
In celebration of SC14, we’ve decided to put together a daily list of some the top tweets from the event. For those unable to attend, we hope this gives you a Read more…
November 19, 2014
When the SC14 show floor opened in New Orleans Monday night, signage everywhere proclaimed this year’s theme: HPC Matters. The new program, first announced at Read more…
November 19, 2014
When the dust settles after its acquisition of the System x business from IBM, Lenovo Group will probably end up with the second largest HPC systems business in Read more…
November 19, 2014
In celebration of SC14, we’ve decided to put together a daily list of some the top tweets from the event. For those unable to attend, we hope this gives you a Read more…
November 18, 2014
This morning at SC14 during their annual breakfast briefing on high performance computing market trends, global analyst firm IDC kicked off with a correction to Read more…
November 18, 2014
Today at Supercomputing 2014, DataDirect Networks lifted the veil a bit more on Infinite Memory Engine (IME), its new software that will employ Flash storage an Read more…
November 17, 2014
The Discover system at NASA’s Center for Climate Simulation was designed with scalability and flexibility in mind, starting with its original nodes in 2006 an Read more…
November 17, 2014
In celebration of SC14, we've decided to put together a daily list of some the top tweets from the event. For those unable to attend, we hope this gives you a w Read more…
November 17, 2014
Traditionally, one of the most exciting opening elements of the annual SC event is the announcement of the list of the Top 500 supercomputers on the planet. The Read more…
November 17, 2014
With so much on the menu at SC with its exceptional program of technical papers, tutorials, research posters, and Birds-of-a-Feather (BOF) sessions, it's diffic Read more…
November 14, 2014
Just when it started to look as though the architectural course had been set for the next wave of large-scale supercomputers, today offered quite a shakeup to t 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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