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MEPHESTO: Modeling Energy-Performance in Heterogeneous SoCs and Their Trade-Offs...

by Mohammad Alaul Haque Monil, Mehmet E Belviranli, Seyong Lee, Jeffrey S Vetter, Allen Malony
Publication Type
Conference Paper
Book Title
The International Conference on Parallel Architectures and Compilation Techniques
Publication Date
Page Numbers
413 to 425
Publisher Location
New York, New York, United States of America
Conference Name
The ACM International Conference on Parallel Architectures and Compilation Techniques (PACT)
Conference Location
Atlanta, Georgia, United States of America
Conference Sponsor
Conference Date

Integrated shared memory heterogeneous architectures are pervasive because they satisfy the diverse needs of mobile, autonomous, and edge computing platforms. Although specialized processing units (PUs) that share a unified system memory improve performance and energy efficiency by reducing data movement, they also increase contention for this memory since the PUs interact with each other. Prior work has investigated performance degradation due to memory contention, but few have studied the relationship of power and energy to memory contention. Moreover, a comprehensive solution that models memory contention for kernel placement on contemporary heterogeneous systems on chip (SoCs) in response to energy and performance has been largely unaddressed.

This paper presents MEPHESTO, a novel and holistic approach for managing this balance. The authors characterize applications and PUs in terms of two memory contention factors - time factors and power factors - to achieve the desired trade-off between energy and performance for collocated kernel execution on heterogeneous systems. The authors believe that this investigation is the first to combine all of these factors and present a simple knob-based approach that expresses the target trade-off. The approach is evaluated on a diverse integrated shared memory heterogeneous system with a CPU, GPU, and programmable vision accelerator. By using an empirical model for memory contention that provides up to 92% accuracy, the kernel collocation approach can provide a near-optimal ordering and placement based on the user-defined, energy-performance trade-off parameter. Moreover, the dynamic programming-based heuristics provide up to 30% better energy or 20% performance benefits when compared with the greedy approaches commonly employed by previous studies.