Datanami
EnterpriseAI
HPCwire Japan
QCwire
HPC & AI Wall Street
NCSA’s Bill Gropp Digs into MPI – Past, Present, and Future
If you work in scientific computing, MPI (message passing interface) is likely a part of your life. It may be hidden underneath the applications you run or you may wrangle with it yourself to dri …
ASC23: Tenth Anniversary Edition – Still the Toughest
The tenth edition of the Asian Supercomputing Challenge took place last week in Hefei, China, and it was tougher than ever. 24 university teams (20 on site, 4 remote) competed for five days to …
Microsoft and ETH Take Aim at Quantum Computing’s Hype (and Promise)
May 2, 2023
A fascinating ACM paper by researchers Torsten Hoefler (ETH Zurich), Thomas Häner (Microsoft*) and Matthias Troyer (Microsoft) – "Disentangling Hype from Pra Read more…
SandboxAQ Launches End-to-End Cryptography Security Suite
April 19, 2023
Worry about data security and the prospect of RSA decryption by future quantum computers has prompted a surge of efforts to advance cryptography tools in recent Read more…
QC Ware Launches Promethium, a Quantum Chemistry SaaS; Targets Hybrid World Near-term
April 17, 2023
Like many in the quantum computing world, particularly quantum algorithm/software developers, QC Ware is focusing near-term on classical and hybrid classical-qu Read more…
2023 Winter Classic: NASA WRF Challenge Results!
April 8, 2023
A wintery mix with a chance for scattered dependencies was the forecast as students tackled the NASA WRF Challenge in the 2023 Winter Classic Invitational Stude Read more…
2023 Winter Classic Oak Ridge Blast Challenge Revealed
April 7, 2023
The close of the 2023 Winter Classic Invitational Student Cluster Competition is coming up fast, and I have to get some material out to you, our vast viewing au Read more…
MLPerf Inference 3.0 Highlights – Nvidia, Intel, Qualcomm and…ChatGPT
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
Nvidia Announces BlueField-3 GA, Oracle Cloud Is Early User
March 21, 2023
Nvidia today announced general availability for its BlueField-3 data processing unit (DPU) along with impressive early deployments including Oracle Cloud Infras Read more…
Tasty CHIPS – New MEC Program to Expand US Prototyping Capabilities Gains Steam
March 16, 2023
Sometime later this year, perhaps around July, the Department of Defense is expected to announce the sites and focus of up to nine hubs associated with the Micr Read more…
ISC 2023 Booth Videos
 |
 |
 |
 |
 |
- Click Here for More Headlines
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.