The aim of this study was to investigate the effects of acute stress on parallel task performance with the Game of Dice Task (GDT) to measure decision making and the Stroop test.  Two previous studies have found that the combination of stress and a parallel task with the GDT and an executive functions task preserved performance on the GDT for a stress group compared to a control group.  The purpose of this study was to create and use a new parallel task with the GDT and the stroop test to elucidate more information about the executive function contributions from the stroop test and to ensure that this parallel task preserves performance on the GDT for the stress group.  Sixteen participants (Mean Age: 26.88) were randomly assigned to either a stress group with the Trier Social Stress Test (TSST) or the control group with the placebo-TSST.  The Positive and Negative Affect Schedule (PANAS) and the State-Trait Anxiety Inventory (STAI) were given before and after the TSST or placebo-TSST and were used as stress indicators.  The results showed a trend towards the stress group performing marginally better than the control group on the GDT but not significantly.  There were no significant differences between the groups for accuracy on the Stroop test trial types.  However, the stress group had significantly slower mean response times on the congruent trial type of the Stroop test, p < .05, though.  This study has shown further evidence that stress and a parallel task together preserve performance on the GDT.

This thesis deals with the problem of finding effective methods for programming and distributing data-parallel applications for heterogeneous multiprocessor systems. These systems are ubiquitous today. They range from embedded devices with low power consumption to high performance distributed systems. The demand for these systems is growing steadily. This is due to the growing number of data-intensive applications and the general growth of digital applications. Systems with multiple devices offer higher performance but unfortunately add complexity to the software development for such systems. Programming heterogeneous multiprocessor systems present several unique challenges compared to single device systems. The first challenge is the programmability of such systems. Despite constant innovations in programming languages and frameworks, they are still limited. They are either platform specific, like CUDA which supports only NVIDIA GPUs, or applied at a low level of abstraction, such as OpenCL. Application developers that design OpenCL programs must manually distribute data to the different devices and synchronize the distributed computations. These capabilities have an impact on the productivity of the developers. To reduce the programming complexity and the development time, this thesis introduces two approaches that automatically distribute and synchronize the data-parallel workloads. Another challenge is the multi-device hardware utilization. In contrast to single-device platforms, the application optimization process for a multi-device system is even more complicated. The application designers need to apply not only optimization strategies specific for a single-device architecture. They need also focus on the careful workload balancing between all the platform processors. For the balancing problem, this thesis proposes a method based on the platform model. The platform model is created with machine learning techniques. Using machine learning, this thesis builds automatically a reliable platform model, which is portable and adaptable to different platform setups, with a minimum manual involvement of the programmers.