May 11, 2017
GPU powerhouse Nvidia's entry into the cloud market is differentiated from public cloud leaders by its focus on delivering development tools for training artifi 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.
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.