Smart data structures : an online machine learning approach to multicore data structures

• Main Author: Eastep, Jonathan M. (Jonathan Michael)
• Other Authors: Anant Agarwal.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2011
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/65967

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Cataloged from student submitted PDF version of thesis. === Includes bibliographical references (p. 175-180). === As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently 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 themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant 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. === by Jonathan M. Eastep. === Ph.D.