June 21, 2013
To wrap up ISC, we wanted to collect some general visualized trends from this year's rankings. In this visual feature, we've provided information on operating systems, key vendors, processor and interconnect technologies and more. While there are a million ways to analyze the... Read more…
June 21, 2011
IDC presented its overview of the market for high performance computing at the International Supercomputing Conference in Germany this week. Tom Tabor reflects on these trends and predictions in this post from Hamburg. Read more…
November 19, 2010
To say the market share for HPC interconnects is imbalanced is a rather dramatic understatement since the vast majority of business is driven by Mellanox. However, there are a few emerging competitors who might be playing a more prominent role in HPC--and by proxy, cloud computing. 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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