In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance parameters, namely: (1) communication to computation ratio (CCR) between 0.1 and 1.0, (2) application size, i.e., task graphs comprising of 50-, 100-, and 300-tasks per graph, and (3) number of cores with 2-, 4-, 8-, and 16-cores execution scenarios. We have also performed experiments using selected real-world applications. The LeTS heuristic reduces overall execution time of applications by exploiting inter-task data locality. Results show that LeTS outperforms state-of-the-art algorithms in amortizing inter-task communication cost.

Peer-to-peer live media streaming over the Internet is becoming increasingly more popular, though it is still a challenging problem. Nodes should receive the stream with respect to intrinsic timing constraints, while the overlay should adapt to the changes in the network and the nodes should be incentivized to contribute their resources. In this work, we meet these contradictory requirements simultaneously, by introducing a distributed market model to build an efficient overlay for live media streaming. Using our market model, we construct two different overlay topologies, tree-based and mesh-based, which are the two dominant approaches to the media distribution. First, we build an approximately minimal height multiple-tree data dissemination overlay, called Sepidar. Next, we extend our model, in GLive, to make it more robust in dynamic networks by replacing the tree structure with a mesh. We show in simulation that the mesh-based overlay outperforms the multiple-tree overlay. We compare the performance of our two systems with the state-of-the-art NewCoolstrea-ming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay.