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Rinnegan: Efficient Resource Use in Heterogeneous Architectures

Sankaralingam Panneerselvam, Michael Swift
University of Wisconsin-Madison
25th International Conference on Parallel Architectures and Compilation Techniques (PACT), 2016

@inproceedings{panneerselvam2016rinnegan,

   title={Rinnegan: Efficient Resource Use in Heterogeneous Architectures},

   author={Panneerselvam, Sankaralingam and Swift, Michael},

   booktitle={Proceedings of the 2016 International Conference on Parallel Architectures and Compilation},

   pages={373–386},

   year={2016},

   organization={ACM}

}

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Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM’s big.LITTLE, AMD’s APUs, and Oracle’s M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by programs using these processing units can vary widely due to contention from multiprogramming, thermal constraints and other issues. In these systems, the decision of where to execute a task must consider not only execution time of the task, but also current system conditions. We built Rinnegan, a Linux kernel extension and runtime library, to perform scheduling and handle task placement in heterogeneous systems. The Rinnegan kernel extension monitors and reports the utilization of all processing units to applications, which then makes placement decisions at user level. The Rinnegan runtime provides a performance model to predict the speedup and overhead of offloading a task. With this model and the current utilization of processing units, the runtime can select the task placement that best achieves an application’s performance goals, such as low latency, high throughput, or real-time deadlines. When integrated with StarPU, a runtime system for heterogeneous architectures, Rinnegan improves StarPU by performing 1.5-2x better than its native scheduling policies in a shared heterogeneous environment.
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