Computer architecture technology is moving towards more heteroge-neous solutions, which will contain a number of processing units with different capabilities that may increase the performance of the system as a whole. How-ever, with increased performance comes increased complexity; complexity that is now barely handled in homogeneous multiprocessing systems. The present study tries to solve a small piece of the heterogeneous puzzle; how can we exploit all system resources in a performance-effective and user-friendly way? Our proposed solution includes a run-time system capable of using a variety of different heterogeneous components while providing the user with the already familiar task-centric programming model interface. Furthermore, when dealing with non-uniform workloads, we show that traditional approaches based on centralized or work-stealing queue algorithms do not work well and propose a scheduling algorithm based on trend analysis to distribute work in a performance-effective way across resources. / <p>QC 20130429</p> / ENCORE

Current task-centric many-core schedulers share a “naive” view of processor architecture; a view that does not care about its thermal, architectural or power consuming properties. Future processor will be more heterogeneous than what we see today, and following Moore’s law of transistor doubling, we foresee an increase in power consumption and thus temperature. Thermal stress can induce errors in processors, and so a common way to counter this is by slowing the processor down; something task-centric schedulers should strive to avoid. The Thermal-Task-Interleaving scheduling algorithm proposed in this paper takes both the application temperature behavior and architecture into account when making decisions. We show that for a mixed workload, our scheduler outperforms some of the standard, architecture-unaware scheduling solutions existing today. / QC 20120215

Graphics processing units function well as high performance computing devices for scientific computing. The non-standard processor architecture and high memory bandwidth allow graphics processing units (GPUs) to provide some of the best performance in terms of FLOPS per dollar. Recently these capabilities became accessible for general purpose computations with the CUDA programming environment on NVIDIA GPUs and ATI Stream Computing environment on ATI GPUs. Many applications in computational science are constrained by memory access speeds and can be accelerated significantly by using GPUs as the compute engine. Using graphics processing units as a compute engine gives the personal desktop computer a processing capacity that competes with supercomputers. Graphics Processing Units represent an energy efficient architecture for high performance computing in flow simulations and many other fields. This document reviews the graphic processing unit and its features and limitations.