May 5, 2015
Today, the United States Postal Service is on its third generation of supercomputers, with each generation more capable than its predecessor. IDC believes the U Read more…
May 1, 2013
This week we're at the IDC User Forum in Tucson, staying cool amidst some heated talks about which processor, coprocessor and accelerator approaches are going to push into the lead in the next few years. To take this pulse, we sat down with IDC's Steve Conway to talk about some general trends that are a tall drink of water for a few key vendors, including Intel, NVIDIA..... Read more…
June 1, 2010
An interview with Steve Conway from IDC and its HPC research division expanding on some of the discussions about market forecasts for HPC--this time in the context of the cloud. Opinions are not necessarily all those of IDC--they are Steve's based on his knowledge of the space. Read more…
Many organizations looking to meet their CAE HPC requirements focus on the HPC on-premises hardware or cloud options. But one surprise that many find is that the bulk of their HPC total cost of ownership (TCO) comes from the complexity of integrating HPC software with CAE applications and in perfectly orchestrating the many technologies to use the hardware and CAE licenses optimally.
This white paper discusses how TotalCAE can significantly reduce TCO by offering turnkey, on-premises HPC systems and public cloud HPC solutions specifically for CAE simulation workloads that include integrated technology and software. The solutions, which TotalCAE fully manages, have allowed its clients to deploy hybrid HPC environments that deliver significant savings of up to 80%, faster-running workflows, and peace of mind since their entire solution is managed by professionals well-versed in HPC, cloud, and CAE technologies.
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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