P4: Phase-based power/performance prediction of heterogeneous systems via neural networks

The emergence of Internet of Things increases the complexity and the heterogeneity of computing platforms. Migrating workload between various platforms is one way to improve both energy efficiency and performance. Effective migration decisions require accurate estimates of its costs and benefits. To date, these estimates were done by either instrumenting the source code/binaries, thus causing high overhead, or by using power estimates from hardware performance counters, which work well for individual machines, but until now have not been accurate for predicting across different architectures. In this paper, we propose P
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, a new Phase-based Power and Performance Prediction framework which identifies cross-platform application power and performance at runtime for heterogeneous computing systems. P
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analyzes and detects machine-independent application phases by characterizing computing platforms offline with a set of benchmarks, and then builds neural network-based models to automatically identify and generalize the complex cross-platform relationships for each benchmark phase. It then leverages these models along with performance counter measurements collected at runtime to estimate performance and power consumption if it were running on a completely different computing platform, including a different CPU architecture, without ever having to run it on there. We evaluate the proposed framework on four commercial heterogeneous platforms, ranging from X86 servers to mobile ARM-based architecture, with 129 industry-standard benchmarks. Our experimental results show that P
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can predict the power and performance changes with only 6.8% and 5.6% error, respectively, even for completely different architectures from the ones applications ran on.