In multicore systems, shared resources such as caches or the memory subsystem can lead to contention between applications running on different cores, entailing reduced performance and poor energy efficiency. The characteristics of individual applications, the assignment of applications to machines and execution contexts, and the selection of processor frequencies have a dramatic impact on resource contention, performance, and energy efficiency.

Non-uniform utilization of functional units in combination with hardware mechanisms such as clock gating leads to different power consumptions in different parts of a processor chip. This in turn leads to non-uniform temperature distributions and problematic local hotspots, depending on the characteristics of the currently running task. The operating system's scheduler, responsible for deciding which task to run at what time, can influence temperature distribution. Our work investigates what the operating system can do to alleviate the problem of hotspots. We propose task activity vectors describing which functional units a task uses to what degree. With the knowledge provided by these vectors, the scheduler can schedule tasks using different units successively, distribute tasks using a particular unit excessively over the system's processors, or mix tasks using different units on a SMT processor. We implemented several vector-based scheduling strategies for Linux. Our evaluations show that vector-based scheduling considerably reduces hotspots.

In this paper, we propose a language model based approach to classify user questions in the context of question answering systems. As categorization paradigm, a Bayes classifier is used to determine a corresponding semantic class. We present experiments with state-of-the-art smoothing methods as well as with some improved language models. Our results indicate that the techniques proposed here provide performance superior to the standard methods, including support vector machines.

Actions usually taken to prevent processors from overheating, such as decreasing the frequency or stopping the execution flow, also degrade performance. Multiprocessor systems, however, offer the possibility of moving the task that caused a CPU to overheat away to some other, cooler CPU, so throttling becomes only a last resort taken if all of a system's processors are hot. Additionally, the scheduler can take advantage of the energy characteristics of individual tasks, and distribute hot tasks as well as cool tasks evenly among all CPUs.This work presents a mechanism for determining the energy characteristics of tasks by means of event monitoring counters, and an energy-aware scheduling policy that strives to assign tasks to CPUs in a way that avoids overheating individual CPUs. Our evaluations show that the benefit of avoiding throttling outweighs the overhead of additional task migrations, and that energy-aware scheduling in many cases increases the system's throughput.