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AI Partners Seek ‘Engine of Scientific Discovery’
December 16, 2020
A research effort built around an automation platform that connects data and analytics applications will focus on advancing the use of AI and machine learning i Read more…
Chaminade University’s Immersion Program Builds Capacity for Data Science in Hawaii, Pacific Region
October 10, 2019
Kuleana is a uniquely Hawaiian value and practice which embodies responsibility to self, community, and the ‘aina' (land). At Chaminade University, a federall Read more…
IDAS: ‘Automagic’ HPC With Training Wheels
September 12, 2019
High-performance computing (HPC) for research is notorious for having steep barriers to entry. For this reason, high-tech disciplines were early adopters, have Read more…
Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark
May 13, 2019
A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…
IBM, Nvidia in AI Data Pipeline, Processing, Storage Union
December 11, 2018
IBM and Nvidia today announced a new turnkey AI solution that combines IBM Spectrum Scale scale-out file storage with Nvidia’s GPU-based DGX-1 AI server to pr Read more…
Nvidia, Oracle Expand Cloud GPU Ties for AI, HPC
October 11, 2018
Oracle is collaborating with Nvidia to bring the GPU leader’s unified AI and HPC platform to the public cloud for accelerating analytics and machine learning workloads. The move makes Oracle the first public cloud vendor to support Nvidia’s HGX-2 platform, the partners said this week. Read more…
Nvidia Platform Pushes GPUs into Machine Learning, High Performance Data Analytics
October 10, 2018
GPU leader Nvidia, generally associated with deep learning, autonomous vehicles and other higher-end enterprise and scientific workloads (and gaming, of course) Read more…
AI Thought Leaders on Capitol Hill
July 14, 2018
On Thursday, July 12, the House Committee on Science, Space, and Technology heard from four academic and industry leaders – representatives from Berkeley Lab, Argonne Lab, GE Global Research and Carnegie Mellon University – on the opportunities springing from the intersection of machine learning and advanced-scale computing. Read more…
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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.
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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.
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