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SLIDE algorithm for training deep neural nets faster on CPUs than GPUs

Rice researchers created a cost-saving alternative to GPU, an algorithm called “sub-linear deep learning engine” (SLIDE) that uses general purpose central processing units (CPUs) without specialized acceleration hardware. “Our tests show that SLIDE is the first smart algorithmic implementation of deep learning on CPU that can outperform GPU hardware acceleration on industry-scale recommendation datasets with large fully connected architectures.”

Deep Learning for Predicting Severe Weather

Researchers from Rice University have introduced a data-driven framework that formulates extreme weather prediction as a pattern recognition problem, employing state-of-the-art deep learning techniques. “In this paper, we show that with deep learning you can do analog forecasting with very complicated weather data — there’s a lot of promise in this approach.”

Panel Discussion: Delivering Exascale Computing for the Oil and Gas Industry

In this video from the 2018 Rice Oil & Gas Conference, Addison Snell from Intersect360 Research leads a panel discussion on Exascale computing. “High-end computing and information technology continues to stand out across the industry as a critical business enabler and differentiator with a relatively well understood return on investment. However, challenges such as constantly changing technology landscape, increasing focus on software and software innovation, and escalating concerns around workforce development still remain.”

Registration Opens for Rice Oil & Gas HPC Conference

Registration is now open for the 2017 Rice Oil & Gas HPC Conference. The event takes place March 15-16 in Houston, Texas. “Join us for the 10th anniversary of the Rice Oil & Gas HPC Conference. OG HPC is the premier meeting place for networking and discussion focused on computing and information technology challenges and needs in the oil and gas industry.”