predictability. In this paper, we propose to use cache space isolation techniques to avoid cache contention for hard realtime tasks running on multicores with shared caches. We present a scheduling strategy for real-time tasks with both timing and cache space constraints, which allows each task to use a fixed

homogeneous and very pow-erful machines. As more servers are added to such clusters, more memory is available for caching data objects across the distributed machines. However the cached objects are dispersed and traditional query scheduling policies that take into account only load balancing do

of cache contention at the same time. The goal is to produce a comprehensive understanding of the relations between program characteristics and run-time behavior in shared-cache systems, meanwhile developing a scalable adaptive contention-aware scheduling system. The preliminary experiments demonstrate

We present a novel characterization of how a pro-gram stresses cache. This characterization permits fast performance prediction in order to simulate and assist task scheduling on heterogeneous clus-ters. It is based on the estimation of stack distance probability distributions. The analysis

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while meeting QoS constraints. First, we present cache-aware metascheduling, which is a novel approach to modifying system execution schedules to increase cache-hit rate and reduce system execution time. Second, we apply cache-aware metascheduling to 11 simulated software systems to create 2 different

interference between tasks but also on the indirect interference between virtual processors and the tasks executing on them. In this paper, we present a cache-aware compositional analysis technique that can be used to ensure timing guarantees of components scheduled on a multicore virtualization platform. Our

-related performance gains. This paper addresses these two issues in an implementation of a cacheaware soft real-time scheduler within Linux, and shows that the use of this scheduler can result in performance improvements that directly result from a decrease in shared cache miss rates.

becomes even more important, particularly in terms of scalability concerns. In this thesis, we consider the problem of cache-aware real-time scheduling on multipro-cessor systems. One avenue for improving real-time performance on multi-core platforms is task partitioning. Partitioning schemes statically

propose a cache-aware memory-scheduling scheme for the backprojection, which can ensure a better load-balancing between GPU processors and the GPU memory. The proposed reshuffling method can be directly applied on existing GPU-accelerated CT reconstruction pipelines. The experimental results show that our