Intel has recently released new server processors for the Xeon and Xeon Phi product lines. Amitai Armon discusses how these processors are used for machine-learning tasks and offers data on their performance for several types of algorithms in both single-node and multinode settings.
Amitai provides performance results for a variety of common machine-learning tasks, demonstrating the relevant capabilities of each processor and what can be achieved through parallelization. In addition, Amitai offers an overview of the relevant software stack available for each processor and explores its deployment and usability.
Amitai Armon is the chief data scientist for Intel’s Advanced Analytics group, which provides solutions for the company’s challenges in diverse domains ranging from design and manufacturing to sales and marketing, using machine learning and big data techniques. Previously, Amitai was the cofounder and director of research at TaKaDu, a provider of water-network analytics software to detect hidden underground leaks and network inefficiencies. The company received several international awards, including the World Economic Forum Technology Pioneers award. Amitai has about 15 years of experience in performing and leading data science work. He holds a PhD in computer science from the Tel Aviv University in Israel, where he previously completed his BSc (cum laude, at the age of 18).
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