Smart data structures: An online learning approach to multicore data structures
Name
Agarwal_Smart data.pdf
Size
1.17 MB
Format
Adobe PDF
Checksum (MD5)
722f12413af884ca9b63a42b75f9a66b
Author(s)
Eastep, Jonathan Michael
Wingate, David
Agarwal, Anant
Date Issued
June 2011
Journal
Proceedings of the 8th ACM international conference on Autonomic computing (ICAC '11)
Publisher
Association for Computing Machinery (ACM)
Citation
Jonathan Eastep, David Wingate, and Anant Agarwal. 2011. Smart data structures: an online machine learning approach to multicore data structures. In Proceedings of the 8th ACM international conference on Autonomic computing (ICAC '11). ACM, New York, NY, USA, 11-20.
Version
Author's final manuscript
Abstract
As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is efficiently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate significant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1998582.1998587
