Graph + AI World:                   

Fast Parallel Similarity Calculations with FPGA Hardware

Graph + AI World Session - Recorded September 2020
The foundation of recommendation is finding similar customers and their purchasing patterns. Yet, if you have 100 million customers it can take hours to do similarity calculations on just 200 features. However, since these calculations can be done in parallel, we show that using an FPGA can allow these calculations to be done in under 30 millisecond. This session will show how using TigerGraph User Defined Functions (UDF), similarity calculations, and therefore product recommendations can be done in real-time as customers visit your web site.

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With FPGA you have enough parallelism, up to 10 million patients on 1 card, you can process in parallel

Kumar Deepak, Xilinx

About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions. 


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