An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores

摘要

Heterogeneous manycore architectures have shown to be highly promising to boost power efficiency through two independent ways: 1) enabling massive thread-level parallelism, called “scale-out” approach, and 2) enabling thread migration between heterogeneous cores, called “scale-up” approach. How to accurately model the profitability of power efficiency of the two ways, particularly in an analytical and computational-effective manner, is essential to reap the power efficiency of such architectures. We propose a comprehensive analytical model to predict the power efficiency from the two independent ways. Given power efficiency is measured by performance per watt, this model is composed of a performance and a power model. The performance model is built by two orthogonal functions and . Function describes the scale-out speedup from multithreading; function presents the scale-up speedup from core heterogeneity. Thus, the performance model can clearly capture the overall speedup of any multithreading and thread-to-core mapping strategies. The power model predicts the power of corresponding scale-out and scale-up configurations. It simultaneously captures the power variations caused by thread synchronization and thread migration between heterogeneous cores. We build both performance and power model in an- analytical way and keep the computational complexity in mind. This merit leads to a suit of comprehensive and low-complexity models for runtime management. These models are validated on large-scale heterogeneous manycore architecture with full-system simulations. For performance prediction, the average error is below 12 percent, lower than that of the state-of-the-art methods. For power prediction, the average error is 7.74 percent. On top of the models, we introduce two heuristic scheduling algorithms, performance-oriented MAX-P and power efficiency-oriented MAX-E, to demonstrate the usage of these models. The results show that MAX-P outperforms the state-of-the-art methods by 18 percent in performance averagely; MAX-E outperforms the baseline by 70 percent in power efficiency on average.