Energy efficiency and performance management of parallel dataflow applications

Parallelizing software is a popular way of achieving high energy efficiency since parallel applications can be mapped on many cores and the clock frequency can be lowered. Perfect parallelism is, however, not often reached and different program phases usually contain different levels of parallelism due to data dependencies. Applications have currently no means of expressing the level of parallelism, and the power management is mostly done based on only the workload. In this work, we provide means of expressing QoS and levels of parallelism in applications for more tight integration with the power management to obtain optimal energy efficiency in multi-core systems. We utilize the dataflow framework PREESM to create and analyze program structures and expose the parallelism in the program phases to the power management. We use the derived parameters in a NLP (Non Linear Programming) solver to determine the minimum power for allocating resources to the applications.

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