Oversubscription of computational resources on multicore desktop systems

The increasing pervasiveness of multicore processors in today's computing systems will increase the demand for techniques to adapt application level parallelism. In this paper we demonstrate the impact of oversubscription in the context of a desktop scenario and show that using too many threads in one application can lead to significant performance loss for other applications within the system environment (in our example more than 30%). We examine how common parallelization tools are trying to exploit parallelism while not causing oversubscription and find that common tools do not offer a mechanism which sufficiently takes into account the computational load of the system. Oversubscription is not easy to detect from an application perspective due to the fair scheduling of common operating systems. Nonetheless, we do present an approach for detecting oversubscription by exploiting a scheduling artifact in the average execution time of a parallel code section

Performance Optimization Strategies for Transactional Memory Applications

This thesis presents tools for Transactional Memory (TM) applications that cover multiple TM systems (Software, Hardware, and hybrid TM) and use information of all different layers of the TM software stack. Therefore, this thesis addresses a number of challenges to extract static information, information about the run time behavior, and expert-level knowledge to develop these new methods and strategies for the optimization of TM applications