Datanami
EnterpriseAI
HPCwire Japan
QCwire
HPC & AI Wall Street
Kubernetes and HPC Applications in Hybrid Cloud Environments – Part II
March 19, 2020
With the rise of cloud services, CIOs are recognizing that applications, middleware, and infrastructure running in various compute environments need a common management and operating model. Maintaining different application and middleware stacks on-premises and in cloud environments, by possibly using different specialized infrastructure and application... Read more…
Democratization of HPC Part 4: Deep Learning for Fluid Flow Prediction in the Cloud
November 1, 2018
This is the fourth and final article demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas. In Read more…
Democratization of HPC Part 3: Ninth Graders Tap HPC in the Cloud to Design Flying Boats
October 18, 2018
This is the third in a series of articles demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas Read more…
Democratization of HPC Part 2: HPC in Personalized Non-invasive Clinical Treatment of Schizophrenia and Parkinson’s
October 4, 2018
This is the second in a series of articles demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application area Read more…
Dassault, NIMHANS, and UberCloud Foster Innovative Schizophrenia Treatment
March 29, 2018
In a series of challenging high performance computing applications in the life sciences, UberCloud’s HPC containers have been packaged recently with several scientific workflows and application data to simulate complex phenomena in human’s heart and brain. Read more…
Stanford University and UberCloud Achieve Breakthrough in Living Heart Simulations
September 21, 2017
Cardiac arrhythmia can be an undesirable and potentially lethal side effect of drugs. During this condition, the electrical activity of the heart turns chaotic, Read more…
Towards Ubiquitous HPC — Passing HPC into the hands of every engineer and scientist
January 7, 2016
Countless case studies demonstrate impressively the importance of HPC for engineering and scientific insight, product innovation, and market competitiveness. Bu Read more…
UberCloud Marketplace for HPC as a Service Goes Live
January 29, 2014
UberCloud is the online community and marketplace where engineers and scientists can discover, try and buy the computing power and expertise on demand they need Read more…
- Click Here for More Headlines
Leading Solution Providers
ISC 2023 Booth Videos
 |
 |
 |
 |
 |
Whitepaper
Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer
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.
Download Now
Sponsored by ANSYS
Whitepaper
How to Save 80% with TotalCAE Managed On-prem Clusters and Cloud
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.
Download Now
Sponsored by TotalCAE
Advanced Scale Career Development & Workforce Enhancement Center
Featured Advanced Scale Jobs:
HPCwire Resource Library
-
Whitepaper: Streamlining AI Data Management
-
Whitepaper: Reducing Bias and Increasing Accuracy in AI Models
-
Whitepaper: A New Standard in CAE Solutions for Manufacturing
-
Whitepaper: Porting CUDA Applications to Run on AMD GPUs
-
Whitepaper: QCT HPC BeeGFS Storage: A Performance Environment for I/O Intensive Workloads
-
Webcasts and webinars: Leveraging Exascale Era Technology for Advanced Computer-Aided Engineering
HPCwire Product Showcase
© 2023 HPCwire. All Rights Reserved. A Tabor Communications Publication
HPCwire is a registered trademark of Tabor Communications, Inc. Use of this site is governed by our Terms of Use and Privacy Policy.
Reproduction in whole or in part in any form or medium without express written permission of Tabor Communications, Inc. is prohibited.