June 29, 2022
MLCommons’ latest MLPerf Training results (v2.0) issued today are broadly similar to v1.1 released last December. Nvidia still dominates, but less so (no gran Read more…
April 4, 2022
Later this spring, ACES – the new ‘composable’ supercomputer being stood up at Texas A&M University – will begin granting Phase One access to early Read more…
March 3, 2022
Graphcore introduced its AI-focused, PCIe-based Intelligent Processing Units (IPUs) six years ago. Since then, the company has done anything but slow down, announcing a second generation of IPUs in 2020 and, over the years, larger and larger IPU-based “IPU-POD” systems — most recently the IPU-POD128 and the IPU-POD256, both announced just a few months... Read more…
December 1, 2021
MLCommons today released its fifth round of MLPerf training benchmark results with Nvidia GPUs again dominating. That said, a few other AI accelerator companies Read more…
October 22, 2021
After launching its second-generation intelligence processing units (IPUs) in 2020, four years after emerging from stealth, Graphcore is now boosting its produc Read more…
September 23, 2021
As Moore’s law slows, HPC developers are increasingly looking for speed gains in specialized code and specialized hardware – but this specialization, in turn, can make testing and deploying code trickier than ever. Now, researchers from Texas A&M University, the University of Illinois at Urbana... Read more…
June 30, 2021
While Nvidia (again) dominated the latest round of MLPerf training benchmark results, the range of participants expanded. Notably, Google’s forthcoming TPU v4 Read more…
September 23, 2020
AI compute platform vendor Graphcore has launched its first formal global channel partner program to promote and boost the sales of its AI processors and blade computing products. The formalized, all-new Graphcore Elite Partner Program follows the company’s past history of working with several... 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.
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
HPCwire is a registered trademark of Tabor Communications, Inc. Use of this site is governed by our Terms of Use and Privacy Policy.
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.