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…
July 14, 2020
In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programm Read more…
January 31, 2020
As the MLPerf benchmark emerges as an industry standard for measuring the performance of machine learning models, its creators said they will phase out the foundational DAWNBench metric. Stanford University researchers announced earlier this month they will end rolling submissions—that is, finished sections of a model... Read more…
April 12, 2016
The high performance computing market is going through a technology transition – the Co-Design transition. As has already been discussed in many articles, this transition has emerged in order to solve the performance bottlenecks of today’s infrastructures and applications, performance bottlenecks that were... Read more…
March 25, 2016
Today SGI's Gabriel Broner announced that the company's SGI UV 3000 system had set two new benchmarking records, demonstrating the machine's advanced throughpu Read more…
April 10, 2013
Amazon's EC2 Cluster Compute instance goes head-to-head with Myrinet 10GigE cluster. Read more…
September 13, 2012
IaaS provider ProfitBricks proclaims noteworthy performance-metrics, pits service against the top two cloud providers. Read more…
June 12, 2012
With the upcoming release of the TOP500 next week, the latest rankings are usually a hot topic of discussion this time of year. Over the past 20 years, the list has proven to be a useful and popular compilation of supercomputers for the HPC community. In this exclusive interview, Professor Hans Meuer, considered by many to be the driving force behind the project, offers his thoughts on the TOP500; its past, present, and future. 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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