We investigate to which extent data partitioning can help
improve the performance of software transactional memory
(STM). Our main idea is that the access patterns of the var-
ious data structures of an application might be sufficiently
different so that it would be beneficial to tune the behav-
ior of the STM for individual data partitions. We evaluate
our approach using standard transactional memory bench-
marks. We show that these applications contain partitions
with different characteristics and, despite the runtime over-
head introduced by partition tracking and dynamic tuning,
that partitioning provides significant performance improve-
ments.