September 8, 2022
The steady maturation of MLCommons/MLPerf as an AI benchmarking tool was apparent in today’s release of MLPerf v2.1 Inference results. Twenty-one organization Read more…
March 30, 2022
From weather sensors and autonomous vehicles to electric grid monitoring and cloud gaming, the world’s edge computing is getting increasingly complex — but the world of HPC hasn’t necessarily caught up to these rapid innovations at the edge. At a panel at Nvidia’s virtual GTC22 (“HPC, AI, and the Edge”), five experts discussed how leading-edge HPC applications... Read more…
January 5, 2022
Edge computing is an approach in which the data is processed and analyzed at the point of origin – the place where the data is generated. This is done to make data more accessible to end-point devices, or users, and to reduce the response time for data requests. HPC-class computing and networking technologies are critical to many edge use cases, and the intersection of HPC and ‘edge’ promises to be a hot topic in 2022. Read more…
September 24, 2021
“Exascale only becomes valuable when it’s creating and using data that we care about,” said Pete Beckman, co-director of the Northwestern-Argonne Institute of Science and Engineering (NAISE), at the most recent HPC User Forum. Beckman, head of an Argonne National Laboratory edge... Read more…
July 22, 2021
“One way of saying what I do for a living is to say that I develop scientific instruments,” said Kate Keahey, a senior fellow at the University of Chicago a Read more…
February 12, 2021
The International Space Station will soon get a delivery of powerful AI, edge and cloud computing tools from HPE and Microsoft Azure to expand technology experi Read more…
May 29, 2020
Developing and deploying applications across heterogeneous infrastructures like HPC or Cloud with diverse hardware is a complex problem. Enabling developers to Read more…
March 23, 2020
Semiconductor market analysts are divided in their assessments of how long and widespread will be the impact of the coronavirus on global foundry revenues and f 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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