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Articles / High Performance Computing / Vectorization

Measuring Graph Analytics Performance

26 Mar 2020CPOL7 min read 14.4K   1  
A graph is a good way to represent a set of objects and the relations between them. Graph analytics is the set of techniques to extract information from connections between entities.
The goal of this article is to show how easy it is to get comprehensive, objective, and reproducible graph analytics performance data without obfuscation or resorting to benchmarking tricks.

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


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Henry Gabb is a Senior Principal Engineer in the Intel Software and Services Group. He first joined Intel in 2000 to help drive parallel computing inside and outside the company. He transferred to Intel Labs in 2010 to become the program manager for various research programs in academia, including the Universal Parallel Computing Research Centers at the University of California at Berkeley and the University of Illinois at Urbana-Champaign. Prior to joining Intel, Henry was Director of Scientific Computing at the U.S. Army Engineer Research and Development Center MSRC, a Department of Defense high-performance computing facility. Henry holds a BS in biochemistry from Louisiana State University, an MS in medical informatics from the Northwestern Feinberg School of Medicine, and a PhD in molecular genetics from the University of Alabama at Birmingham School of Medicine. He has published extensively in computational life science and high-performance computing. Henry recently rejoined Intel after spending four years working on a second PhD in information science at the University of Illinois at Urbana-Champaign, where he applied informatics and machine learning to problems in healthcare and chemical exposure from everyday consumer products.

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