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Hybrid Code-Data Prefetch-Aware Multiprocessor Task Graph Scheduling

Hybrid Code-Data Prefetch-Aware Multiprocessor Task Graph Scheduling,10.1109/DSD.2011.80,Morteza Damavandpeyma,Sander Stuijk,Twan Basten,Marc Geilen,H

Hybrid Code-Data Prefetch-Aware Multiprocessor Task Graph Scheduling  
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The ever increasing performance gap between processors and memories is one of the biggest performance bottlenecks for computer systems. In this paper, we propose a task scheduling technique that schedules an application, modeled with a task graph, on a multiprocessor system-on- chip (MPSoC) that contains a limited on-chip memory. The proposed scheduling technique explores the trade-off between executing tasks in a code-driven (i.e. executing parallel tasks) or data-driven (i.e. executing pipelined tasks) manner to minimize the run-time of the application. Our static scheduler identifies those task sequences in which it is useful to use a code- driven execution and those task sequences that benefit from a data-driven execution. We extend the proposed technique to consider prefetching when choosing a suitable task order. The technique is implemented using an integer linear programming framework. To evaluate the effectiveness of the technique, we use an application from the multimedia domain and a synthetic task graph that is used in related work. Our experimental results show that our scheduler is able to reduce the run-time of an MP3 decoder application by 8% compared to a commonly used heuristic scheduler. Keywords-Code generation, run-time minimization, scratch- pad memory, scheduling, ILP
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