  In current microarchitectures, due to the complex memory hierarchies and
different latencies on memory accesses, thread and data mapping are important
issues to improve application performance. Software transactional memory (STM)
is an abstraction used for thread synchronization, replacing the use of locks
in parallel programming. Regarding thread and data mapping, STM presents new
challenges and mapping opportunities, since (1) STM can use different conflict
detection and resolution strategies, making the behavior of the application
less predictable and; (2) the STM runtime has precise information about shared
data and the intensity with each thread accesses them. These unique
characteristics provide many opportunities for low-overhead, but precise
statistics to guide mapping strategies for STM applications. The main objective
of this paper is to survey the existing work about thread and data mapping that
uses solely information gathered from the STM runtime to guide thread and data
mapping decisions. We also discuss future research directions within this
research area.

Distributed, Parallel, and Cluster Computing