Watt matters most? Design space exploration of high-performance microprocessors for power-performance efficiency
Computer systems have evolved significantly in the last years leading to high-performance systems. This, however, has come with a cost of large power dissipation. As such, power-awareness has become a major factor in processor design. Therefore, it is important to have a complete understanding of the power and performance behavior of all processor components. In order to achieve this, the current work presents a comprehensive analysis of power-performance efficiency for different high-end microarchitecture configurations using three different workloads: multimedia, scientific, and database. The objectives of this work are: (1) to analyze and compare the power-performance efficiency for different workloads; (2) to present a sensitivity analysis for the microarchitecture parameters in order to identify which ones are more sensitive to changes in terms of power-performance efficiency; and (3) to propose power-performance efficient configurations for each workload. The simulation results show that the multimedia workload is the one achieving the highest efficiency but the database workload is the most sensitive to parameter changes. In addition, the results also show that the parameter sensitivity depends significantly on the workload. While the issue width and clock frequency present very high sensitivity across all workloads (approximately 100%), for the database workload, the. first-level instruction cache size shows an even higher sensitivity (149%). The correct configuration of these microarchitecture parameters is essential. A careless configuration of a single parameter from a baseline setup may result in a loss of the power-performance efficiency of up to 99%. Finally, carefully tuning multiple parameters simultaneously may result in gains up to 154% over the power-performance efficiency of the baseline configuration.
power-performance efficiency sensitivity analysis efficient configuration high-end microprocessor design space analysis workload characterization