February 20, 2020
Researchers who use supercomputers for science typically don't limit themselves to one system. They move their projects to whatever resources are available, oft Read more…
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…
October 20, 2016
HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…
August 18, 2016
With early adopters of application container technology completing early testing in multi-tenant settings, potential performance issues are beginning to surface Read more…
May 31, 2016
Univa today announced general availability of Grid Engine 8.4.0. The latest version of Grid Engine includes many new features including expanded support for Docker containers as well as “preview support” for Intel’s latest Xeon Phi code named Knights Landing processor. Univa also reports fixing more than 80 prior issues. Leading the container enhancements, users can now automatically dispatch and run jobs in Docker containers, from a user specified Docker image. Read more…
February 3, 2016
The latest version of convergence – blending traditional HPC and big data computing into a ‘single’ environment – dominates much of the conversation in Read more…
August 7, 2015
The explosive growth in data coming out of experiments in cosmology, particle physics, bioinformatics and nuclear physics is pushing computational scientists to 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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