October 7, 2021
At its VMworld 2021 event this week, VMware introduced Project Capitola, a new company-led effort to use its vSphere software to virtualize application access t Read more…
March 16, 2021
After almost 10 years of investments and flagging progress, Micron announced today (Tuesday) that it is ending its involvement in 3D XPoint, the non-volatile me Read more…
February 17, 2021
Samsung today announced what it’s calling the industry’s first high bandwidth memory (HBM) memory with built-in AI processing capability. The new device – Read more…
February 2, 2021
Frontera, the world’s largest academic supercomputer housed at the Texas Advanced Computing Center (TACC), is big both in terms of number of computational nodes and the capabilities of the large memory “fat” compute nodes. A couple of recent use cases demonstrate how academic researchers... Read more…
January 21, 2021
Pursuit of in-memory computing has long been an active area with recent progress showing promise. Just how in-memory computing works, how close it is to practic Read more…
January 5, 2021
Competition to leverage new memory and storage hardware with new or improved software to create better storage/memory schemes has steadily gathered steam during Read more…
November 16, 2020
Nvidia has doubled the memory of its previous supercomputing GPUs with its new A100 80GB GPU, which aims to drive new levels of supercomputing performance in a wide variety of uses, from AI and ML research to engineering and more. The new A100 80GB GPU comes just six months... Read more…
October 21, 2020
In a sign that its 3D XPoint memory technology is gaining traction, Intel Corp. is departing the NAND flash memory and storage market with the sale of its manuf 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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