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Dynamic Feature Selection for Machine-Learning Based Concurrency Regulation in STM.

, , , and . PDP, page 68-75. IEEE Computer Society, (2014)

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Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning., , , and . NCCA, page 46-53. IEEE Computer Society, (2014)A framework for high performance simulation of transactional data grid platforms., , , , , , , , and . SimuTools, page 63-72. ICST/ACM, (2013)Machine learning-based thread-parallelism regulation in software transactional memory., , , and . J. Parallel Distributed Comput., (2017)Machine Learning-Based Self-Adjusting Concurrency in Software Transactional Memory Systems., , , and . MASCOTS, page 278-285. IEEE Computer Society, (2012)Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches., , , , and . Transactional Memory, volume 8913 of Lecture Notes in Computer Science, Springer, (2015)Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory., , , and . CCGRID, page 81-91. IEEE Computer Society, (2014)Regulating Concurrency in Software Transactional Memory: An Effective Model-based Approach., , , , and . SASO, page 31-40. IEEE Computer Society, (2013)Auto-tuning of Cloud-Based In-Memory Transactional Data Grids via Machine Learning., , , and . NCCA, page 9-16. IEEE Computer Society, (2012)Automatic Tuning of the Parallelism Degree in Hardware Transactional Memory., , , and . Euro-Par, volume 8632 of Lecture Notes in Computer Science, page 475-486. Springer, (2014)Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs., , and . NCCA, page 105-112. IEEE Computer Society, (2014)