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Finding Parallelism in General-Purpose Linear Programming

Thuerck, Daniel and Naumov, Maxim and Pfetsch, Marc E. and Goesele, Michael (2017):
Finding Parallelism in General-Purpose Linear Programming.
In: GTC 2017, San José, CA, USA, [Conference or Workshop Item]

Abstract

Get to know two different techniques in retrieving parallelism hidden in a general purpose linear programs (LPs) that are broadly used in operations research, computer vision, and machine learning. With conventional solvers often being restricted to serial computation, we'll show two ways of retrieving inherent parallelism, using: (1) parallel sparse linear algebra techniques with an interior-point method, and (2) a higher-level automatic LP decomposition. After a quick introduction to the topic, we'll present details and results for a diverse range of applications on the GPU.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Thuerck, Daniel and Naumov, Maxim and Pfetsch, Marc E. and Goesele, Michael
Title: Finding Parallelism in General-Purpose Linear Programming
Language: English
Abstract:

Get to know two different techniques in retrieving parallelism hidden in a general purpose linear programs (LPs) that are broadly used in operations research, computer vision, and machine learning. With conventional solvers often being restricted to serial computation, we'll show two ways of retrieving inherent parallelism, using: (1) parallel sparse linear algebra techniques with an interior-point method, and (2) a higher-level automatic LP decomposition. After a quick introduction to the topic, we'll present details and results for a diverse range of applications on the GPU.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Graphics, Capture and Massively Parallel Computing
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Exzellenzinitiative > Graduate Schools
Exzellenzinitiative
Event Title: GTC 2017
Event Location: San José, CA, USA
Date Deposited: 15 May 2017 08:38
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