April 18, 2016
The fifth annual Asia Student Supercomputer Challenge (ASC16) got off to an exciting start this morning at the Huazhong University of Science and Technology (HUST) in Wuhan, the capital of Hubei province, China. Read more…
April 21, 2014
This morning at 8am local time, the final round of the 2014 Asia Student Supercomputer Challenge (ASC14) commenced in Guangzhou, China. The setting for the ch Read more…
December 6, 2013
Efforts to field the first exascale supercomputer are currently in play by such nations as China, Japan, the US, and the EU. Not only would this achievement pro Read more…
January 25, 2011
This week IBM announced another addition in its string of cloud computing data center initiatives rooted in the Asia-Pacific region. This brings the company to over $100 million in investment in APAC as analyst figures continue to match this sense of hope for the region's vast market. Read more…
January 19, 2010
China and Singapore gear up petascale efforts. 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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