Survey of machine learning application in transactional memory

Transactional memory (TM) is a paradigm that removes the need for using locks and makes it easier for programmers because it reduces work when it comes to synchronization. TM enables the atomicity and isolation of concurrent threads. TM can be realized in software (STM) or with hardware support (HTM) or hybrid. In this survey different machine learning (ML) techniques are explored to improve performance, energy effectiveness, simplicity, and benefits of using TM. Various methods as thread mapping, self-adjusting concurrency, etc. are combined with machine learning algorithms. After presenting the techniques, the obtained results are discussed.