August 11, 2022
A Google-led program to design and manufacture chips for free is becoming popular among researchers and computer enthusiasts. The search giant's open silicon program is providing the tools for anyone to design chips, which then get manufactured. Google foots the entire bill, from a chip's conception to delivery of the final product in a user's hand. Google's... Read more…
May 30, 2022
During a special address at ISC today, general manager and vice president of Accelerated Computing at Nvidia, Ian Buck, shared promising news for the future of Read more…
February 3, 2022
Commentary -- In the second of a series of guest posts on heterogeneous computing, James Reinders, who returned to Intel last year after a short “retirement, 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…
June 15, 2017
As open source hardware gains traction, the potential for a completely open source supercomputing system becomes a compelling proposition, one that is being inv Read more…
October 5, 2015
When ESnet, the Department of Energy’s (DOE) Energy Sciences Network, unveiled its online interactive network portal called MyESnet in July of 2011, the react Read more…
August 7, 2015
The explosive growth in data coming out of experiments in cosmology, particle physics, bioinformatics and nuclear physics is pushing computational scientists to Read more…
October 2, 2014
The Secretary of Energy Advisory Board (SEAB) Task Force on Next Generation High Performance Computing (HPC) released a report in August addressing the steps re 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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