May 25, 2023
As HPC and AI continue to rapidly advance, the alluring vision of nuclear fusion and its endless zero-carbon, low-radioactivity energy is the sparkle in many a Read more…
November 14, 2022
Over the past months, Nvidia has put a spotlight on its OVX hardware – purpose-built systems aimed at its Omniverse digital twins platform. Now, at SC22, Nvid Read more…
April 10, 2013
Randall J. Leveque, Professor of Applied Mathematics at the University of Washington in Seattle, will be conducting a free course that brings the principles of parallelism in high performance computers to those in scientific computing. Read more…
July 31, 2012
Traditionally running scientific workloads in AWS provides a diverse toolkit that allows researchers to easily sling data around different time zones, regions, or even globally once the data is inside of the infrastructure sandbox. However, getting data in and out of AWS has historically been more of a challenge. Cycle Computing's Andrew Kaczorek and Dan Harris offer some helpful tips on optimizing ingress and egress transfers. Read more…
July 18, 2011
Software engineering is still something that gets too little attention from the technical computing community, much to the detriment of the scientists and engineers writing the applications. Greg Wilson has been on a mission to remedy that, mainly through his efforts at Software Carpentry, where he is the project lead. HPCwire asked Wilson about the progress he's seen over the last several years and what remains to be done. Read more…
October 19, 2010
Last week at their eScience Workshop at the University of California, Berkeley Microsoft Research announced two key technological progress points related to their Azure cloud. The advancements are currently serving researchers in ecological studies as well as biology and further demonstrate the potential of their cloud offering in further scientific computing projects. Read more…
July 13, 2010
The announcement this morning that Amazon is offering Cluster Compute Instances for EC2 specifically for the needs of HPC users might just be that long-awaited game-changer when it comes to the viability of scientific computing in the public cloud. While it is fresh from a private beta and the results are promising, only time will tell to what degree users will snatch up this opportunity to have supercomputing power on demand. Read more…
July 9, 2010
Researchers from Berkeley Lab are looking at different options available for scientific computing users to move beyond physical infrastructure, including the possibility of deploying public clouds. A recently-published study of Amazon EC2's handling of data from the Nearby Supernova Factory sheds light on putting large-scale scientific computing into the cloud in practice and in theory. 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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