Profiling Transactional Memory Applications

Transactional memory (TM) has become an active research area as it promises to simplify the development of highly scalable parallel programs. Scalability is quickly becoming an essential software requirement as successive commodity processors integrate ever larger numbers of cores. Non-trivial TM applications to test TM implementations have only recently begun to emerge, but have been written in different programming languages, using different TM implementations, making analysis difficult. We ported the popular non-trivial TM applications from the STAMP suite (Genome, KMeans, and Vacation), and Lee-TM to DSTM2, a software TM implementation, and built into it a framework to profile their execution. This paper investigates which profiling information is most relevant to understanding the performance of these non-trivial TM applications using up to 8 processors. We report commonly used transactional execution metrics and introduce two new metrics that can be used to profile TM applications.