May 17, 2023
After nearly seven years of service, thousands of user projects, and tens of billions of compute hours, the Cori supercomputer at the National Energy Research S Read more…
February 7, 2023
Time to finally(!) clear the 2022 decks and get the rest of the 2022 Great American Supercomputing Road Trip content out into the wild. The last part of the yea Read more…
January 6, 2022
How will programming future systems differ from current practice? This is an ever-present question in computing. Yet it has, perhaps, never been more pressing g Read more…
January 21, 2021
In the 1970s, scientists theorized the existence of axions: particles born in the hearts of stars that, when exposed to a magnetic field, become light particles Read more…
December 8, 2020
In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming Read more…
May 1, 2020
The COVID-19 pandemic is producing massive amounts of data – and that data is producing a positive avalanche of academic literature. To help sift through thos Read more…
February 25, 2020
As HPC datacenters scale up, improving efficiency is crucial to avoiding correspondingly large energy use (and the ensuing high costs and large carbon footprint Read more…
July 25, 2019
In mid-April, Kathy Yelick announced she would step down as Associate Laboratory Director (ALD) for the Computing Sciences organization at Lawrence Berkeley National Laboratory, a position she has held since September 2010, in addition to heading NERSC from 2008 through 2012. Yelick will return to campus in January 2020... 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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