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On understanding the energy consumption of ARM-based multicore servers
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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Predictive models enable a better understanding of the performance characteristics of applications on multicore systems. Previous work has utilized performance counters in a system-centered approach to model power consumption for the system, CPU, and memory components. Often, these approaches use the same group of counters across different applications. In contrast, we develop application-centric models (based upon performance counters) for the runtime and power consumption of the system, CPU, and memory components. Our work analyzes four Hybrid (MPI/OpenMP) applications: the NAS Parallel Multizone Benchmarks (BT-MZ, SP-MZ, LU-MZ) and a Gyrokinetic Toroidal Code, GTC. Our models show that cache utilization (L1/L2), branch instructions, TLB data misses, and system resource stalls affect the performance of each application and performance component differently. We show that the L2 total cache hits counter affects performance across all applications. The models are validated for the system and component power measurements with an error rate less than 3%.