April 18, 2017
HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…
January 26, 2017
If you are looking for guidance with programming and working with Intel's Xeon Phi (Knights Landing) processors, a solid resource was posted today on the Partne Read more…
November 2, 2016
The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…
April 11, 2016
Deep learning has inspired a gold rush of technology innovation across a wide range of markets from Internet search, to social media, to real-time robotics, sel Read more…
March 23, 2016
It’s a given that modern oil and gas exploration couldn’t exist without increasingly powerful supercomputers. Today's machines enable new capabilities, impr Read more…
February 22, 2016
Intel® Scalable System Framework, an advanced approach for developing scalable, balanced and efficient HPC systems, is paving the path to Exascale by incorpora Read more…
September 14, 2015
Strip-Mining for Vectorization is the focus of the second installment of a 3-part educational series from Colfax International introducing select topics on opti Read more…
August 3, 2015
Our Earth may be round, but the universe appears to be relatively flat. At least that’s the word from the Stephen Hawking Centre for Theoretical Cosmology at 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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