Amol Deshpande, Department of Computer Science
University of Maryland
Patrick Valduriez
Inria and Biology Computational Institute (IBC)
Abstract: The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm.
Benny Kimelfeld, Technion
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
Rachel Pottinger, Department of Computer Science
University of British Columbia
Users are faced with an increasing onslaught of data, whether it's in their choices of movies to watch, assimilating data from multiple sources, or finding information relevant to their lives on open data registries.
Daniel Lemire
Université Télug
Maximizing performance in data engineering is a daunting challenge. We present some of our work on designing faster indexes, with a particular emphasis on compressed indexes. Some of our prior work includes (1) Roaring indexes which are part of multiple big-data systems such as Spark, Hive, Druid, Atlas, Pinot, Kylin, (2) EWAH indexes are part of Git (GitHub) and included in major Linux distributions.