April 26, 2022
In the third of a series of guest posts on heterogeneous computing, James Reinders shares experiences surrounding the creation of ASCI Red and ties that system' Read more…
October 9, 2015
The High Performance Computing Center Stuttgart (HLRS), member of the Gauss Centre for Supercomputing, today reported completion of the second upgrade of its su Read more…
June 29, 2015
Failure to incorporate big data computing insights into efforts to achieve exascale computing would be a critical mistake argue Daniel Reed and Jack Dongarra in Read more…
June 8, 2015
The future of high performance computing is now being defined both in how it will be achieved and in the ways in which it will impact diverse fields in science Read more…
March 26, 2015
The Indian government has approved a seven-year supercomputing program worth $730 million (Rs. 4,500-crore) intended to restore the nation's status as a world- Read more…
May 28, 2013
When so many folks from the HPC community come at us with credible details about something as important as the next top system on the planet, it's hard to ignore. To quiet things down (and hopefully bring forth more information) we've published the consistent details about what we know from (very) credible sources.about this year's upcoming Top500 announcement. While unconfimed, we have.... Read more…
May 3, 2013
For the largest computer systems in the world, keeping IT assets safe presents a unique set of challenges. Read more…
April 15, 2013
Getting scientific applications to scale across Titan's 300,000 compute cores means there will be bugs. Finding those bugs is where Allinea DDT comes in. 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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