COP: Planning conflicts for faster parallel transactional machine learning

Faisal Nawab, Divyakant Agrawal, Amr El Abbadi, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Machine learning techniques are essential to extracting knowledge from data. The volume of data encourages the use of parallelization techniques to extract knowledge faster. However, schemes to parallelize machine learning tasks face the trade-off between obeying strict consistency constraints and performance. Existing consistency schemes require expensive coordination between worker threads to detect conflicts, leading to poor performance. In this work, we consider the problem of improving the performance of multi-core machine learning while preserving strong consistency guarantees. We propose Conflict Order Planning (COP), a consistency scheme that exploits special properties of machine learning workloads to reduce the overhead of coordination. What is special about machine learning workloads is that the dataset is often known prior to the execution of the machine learning algorithm and is reused multiple times with different settings. We exploit this prior knowledge of the dataset to plan a partial order for concurrent execution. This planning reduces the cost of consistency significantly because it allows the use of a light-weight conflict detection operation that we call ReadWait. We demonstrate the use of COP on a Stochastic Gradient Descent algorithm for Support Vector Machines and observe better scalability and a speedup factor between 2-6x when compared to other consistency schemes.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2017
Subtitle of host publication20th International Conference on Extending Database Technology, Proceedings
EditorsBernhard Mitschang, Volker Markl, Sebastian Bress, Periklis Andritsos, Kai-Uwe Sattler, Salvatore Orlando
Number of pages12
ISBN (Electronic)9783893180738
StatePublished - 2017
Externally publishedYes
Event20th International Conference on Extending Database Technology, EDBT 2017 - Venice, Italy
Duration: Mar 21 2017Mar 24 2017

Publication series

NameAdvances in Database Technology - EDBT
ISSN (Electronic)2367-2005


Conference20th International Conference on Extending Database Technology, EDBT 2017


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