November 16, 2022
Europe’s sovereign approach to exascale computing is complicating plans for U.S. chipmakers to breakthrough in the market, and in the process, empowering local chipmakers. For one, a European chip startup called SiPearl is emerging as an early... Read more…
November 12, 2022
Chipmakers regularly indulge in a game of brinkmanship, with an example being Intel and AMD trying to upstage one another with server chip launches this week. But each of those companies are in different positions, with AMD playing its traditional role of a scrappy underdog trying to unseat the behemoth Intel... Read more…
April 12, 2021
Today at Nvidia’s annual spring GPU Technology Conference (GTC), held virtually once more due to the pandemic, the company unveiled its first ever Arm-based CPU, called Grace in honor of the famous American programmer Grace Hopper. The announcement of the new... Read more…
November 27, 2020
As HPE’s chief technology officer for artificial intelligence, Dr. Eng Lim Goh devotes much of his time talking and consulting with enterprise customers about Read more…
July 31, 2020
A machine programming framework for heterogeneous computing championed by Intel Corp. and university partners is built around an automated engine that analyzes Read more…
September 15, 2016
Among the computing challenges presented by big data is the scattering of unstructured items across huge datasets. Pulling together that data from arbitrary loc Read more…
August 30, 2016
After offering OpenPower Summit attendees a limited preview in April, IBM is unveiling further details of its next-gen CPU, Power9, which the tech mainstay is Read more…
December 1, 2015
Contrary to conventional thinking, GPUs are often not the best vehicles for big data visualization. In this commentary, I discuss several key technical reasons 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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