Victor Bittorf
University of Wisconsin-Madison
15 Papers
275 Citations
Victor Bittorf is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Computer science & Linear programming. The author has an hindex of 11, co-authored 14 publications. Previous affiliations of Victor Bittorf include Cloudera.
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Papers
•Proceedings Article
Impala: A Modern, Open-Source SQL Engine for Hadoop.
Marcel Kornacker,Alexander Behm,Victor Bittorf,Taras Bobrovytsky,Casey Ching,Alan Choi,Justin Erickson,Martin Grund,Daniel Hecht,Matthew Jacobs,Ishaan Joshi,Lenni Kuff,Dileep Kumar,Alex Leblang,Nong Li,Ippokratis Pandis,Henry Noel Robinson,David Rorke,Silvius Rus,John Russell,Dimitris Tsirogiannis,Skye Wanderman-Milne,Michael Yoder +22 more
- 01 Jan 2015
TL;DR: This paper presents Impala from a user’s perspective, gives an overview of its architecture and main components and briefly demonstrates its superior performance compared against other popular SQL-on-Hadoop systems.
•Posted Content
MLPerf Training Benchmark.
Peter Mattson,Christine Cheng,Cody Coleman,Greg Diamos,Paulius Micikevicius,David A. Patterson,Hanlin Tang,Gu-Yeon Wei,Peter Bailis,Victor Bittorf,David Brooks,Dehao Chen,Debojyoti Dutta,Udit Gupta,Kim Hazelwood,Andrew Hock,Xinyuan Huang,Atsushi Ike,Bill Jia,Daniel Kang,David Kanter,Naveen Kumar,Jeffery Liao,Guokai Ma,Deepak Narayanan,Tayo Oguntebi,Gennady Pekhimenko,Lillian Pentecost,Vijay Janapa Reddi,Taylor Robie,Tom St. John,Tsuguchika Tabaru,Carole-Jean Wu,Lingjie Xu,Yamazaki Masafumi,Cliff Young,Matei Zaharia +36 more
TL;DR: MLPerf as discussed by the authors is an ML benchmark that overcomes three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time-to-solution exhibits high variance.
274
An asynchronous parallel stochastic coordinate descent algorithm
TL;DR: An asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate on general convex functions.
•Proceedings Article
Brainwash: A data system for feature engineering
Michael R. Anderson,Dolan Antenucci,Victor Bittorf,Matthew Burgess,Michael Cafarella,Arun Kumar,Feng Niu,Yongjoo Park,Christopher Ré,Ce Zhang +9 more
- 01 Jan 2013
TL;DR: This work proposes brainwash, a vision for a feature engineering data system that could dramatically ease the ExploreExtract-Evaluate interaction loop that characterizes many trained system projects.
Impala: Eine moderne, quellen-offene SQL Engine für Hadoop
Marcel Kornacker,Alexander Behm,Victor Bittorf,Taras Bobrovytsky,Casey Ching,Alan Choi,Justin Erickson,Martin Grund,Daniel Hecht,Matthew Jacobs,Ishaan Joshi,Lenni Kuff,Dileep Kumar,Alex Leblang,Nong Li,Ippokratis Pandis,Henry Noel Robinson,David Rorke,Silvius Rus,John Russel,Dimitris Tsirogiannis,Skye Wanderman-Milne,Michael Yoder +22 more
- 01 Jan 2016
TL;DR: Impala as discussed by the authors is a modernes, massiv paralleles Datenbanksystem, whose goal is to enable klassische SQL-Abfragen with geringer Latenz und Laufzeit auszufuhren.
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