July 1, 2021
AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…
April 14, 2021
Deep learning (DL) applications have unique architectural characteristics and efficiency requirements. Hence, the choice of computing system has a profound impa Read more…
March 18, 2021
Now that AMD unveiled its latest third-generation Epyc CPU product line for HPC, enterprise and cloud workloads on March 15, the company’s server and services partners continue to announce their own plans for bringing Epyc-equipped products to market. Read more…
August 12, 2020
Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-512 optimizations for the NAMD scalable molecular dynamics code. These optimizations will be incorporated into release 2.15 with patches available for earlier versions. Read more…
August 8, 2019
From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…
May 13, 2019
The Securities Technology Analysis Center (STAC) issued a report Friday comparing the performance of Intel's Cascade Lake processors with previous-gen Skylake u Read more…
May 2, 2019
On the eve of its 50th anniversary, Advanced Micro Devices (AMD) reported sales of $1.27 billion for Q1 2019, down 10 percent quarter-over-quarter and 23 percen Read more…
December 11, 2018
For several years now the big cloud providers – Amazon, Microsoft Azure, Google, et al – have been transforming from technology consumers into technology cr 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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