Amol Deshpande, Department of Computer Science
University of Maryland
Benny Kimelfeld, Technion
Kareem El Gebaly, PhD candidate
David R. Cheriton School of Computer Science
The process of analyzing relational data typically involves tasks facilitating gaining familiarity or insights and coming up with findings or conclusions based on the data. This process is usually practiced by data experts (data scientists) that share their output with potentially less data expert audience (everyone).
Heng Ji, Rensselaer Polytechnic Institute
Aditya Parameswaran, Department of Computer Science
University of Illinois-Urbana Champaign
Rumi Chunara, Computer Science and in Global Public Health
New York University
Lei Zou, Institute of Computer Science and Technology
Peking University
In this talk, I focus on accelerating a widely employed computing pattern — set intersection, to boost a group of relevant graph algorithms. Graph’s adjacency-lists can be naturally considered as node sets, thus set intersection is a primitive operation in many graph algorithms. We propose QFilter, a set intersection algorithm using SIMD instructions. QFilter adopts a merge-based framework and compares two blocks of elements iteratively by SIMD instructions.
Barzan Mozafari, Department of Computer Science and Engineering
University of Michigan
Speaker: Oliver Kennedy, University at Buffalo
Chang Ge, PhD candidate
David R. Cheriton School of Computer Science