September 21, 2017
Google has taken down the notice on its cloud platform website that says Nvidia Tesla P100s are “coming soon.” That's because the search giant has announced Read more…
August 11, 2015
Despite high initial interest, HPC in the cloud never achieved significant adoption levels, mainly being relegated to low-hanging "pleasingly parallel" fruit a Read more…
September 15, 2014
Many IT organizations are seeking a new approach to the data management challenges presented when using multiple clouds. In particular, they want an approach th Read more…
August 18, 2014
Distributed computing has undergone many permutations, from its roots in grid computing to support large scientific endeavors to Sun-style utility computing, to Read more…
May 10, 2013
The private industry least likely to adopt public cloud services for data storage are financial institutions. Holding the most sensitive and heavily-regulated of data types, personal financial information, banks and similar institutions are mostly moving towards private cloud services – and doing so at great cost. Read more…
April 17, 2013
After a lengthy incubation phase, Microsoft is finally ready to release its IaaS product into the wild. AWS, look out. Read more…
March 14, 2013
The top research stories of the week include the 2012 Turing Prize winners; an examination of MIC acceleration in short-range molecular dynamics simulations; a new computer model to help predict the best HIV treatment; the role of atmospheric clouds in climate change models; and more reliable HPC cloud computing. Read more…
December 4, 2012
AWS used its first ever customer and partner conference, AWS re: Invent, held last week in Las Vegas, as a launch pad for some major company news. During their respective keynotes, AWS Senior Vice President Andrew Jassy revealed a brand new data warehouse service, AWS Redshift, and another price cut for the S3 storage service, while Amazon.com CTO Werner Vogels announced two super-sized EC2 Instance Types, and another new service, the AWS Data Pipeline. 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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