Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
Simple, fast, and practical non-blocking and blocking concurrent queue algorithms
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology
ICS '97 Proceedings of the 11th international conference on Supercomputing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Skiplist-Based Concurrent Priority Queues
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Automatically Tuned Linear Algebra Software
Automatically Tuned Linear Algebra Software
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
PetaBricks: a language and compiler for algorithmic choice
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Online resource allocation using decompositional reinforcement learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Adaptive job routing and scheduling
Engineering Applications of Artificial Intelligence
The Art of Multiprocessor Programming
The Art of Multiprocessor Programming
OPODIS'07 Proceedings of the 11th international conference on Principles of distributed systems
An operating system for multicore and clouds: mechanisms and implementation
Proceedings of the 1st ACM symposium on Cloud computing
Proceedings of the 7th international conference on Autonomic computing
Smartlocks: lock acquisition scheduling for self-aware synchronization
Proceedings of the 7th international conference on Autonomic computing
Flat combining and the synchronization-parallelism tradeoff
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
STAPL: standard template adaptive parallel library
Proceedings of the 3rd Annual Haifa Experimental Systems Conference
ECML'05 Proceedings of the 16th European conference on Machine Learning
Reactive behavior in object-oriented applications: an analysis and a research roadmap
Proceedings of the 12th annual international conference on Aspect-oriented software development
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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.