Multivariate Performance and Power Prediction of Algorithms on Simulation-Based Hardware Models
Abstract: Power-aware computing has become increasingly important while considering the selection of computer systems to run the software. Several computer systems can have similar performance profiles with different power consumption for a given software. By accurately predicting the performance and power consumption of a software execution on different hardware systems, computer systems can be selected, which is a challenging problem. In this paper, we propose a novel multivariate prediction framework that predicts both performance and power consumption for a given software on unknown hardware identified by key hardware features. To measure the performance of our model, we have selected different software workloads according to their computation and memory access patterns consisting of kernels and benchmarks used in real-world applications. We have build 475 simulation-based hardware models with different instruction-set-architectures (ISAs) in the Gem5 simulator that represents computer systems of present times. We have trained our multivariate model on 60% of simulation-based hardware models, and the remaining 40% of hardware models used for prediction. Our result shows a prediction accuracy of greater than 95% for all the software workload for both performance and power consumption.
Keywords:machine learning, computer architecture, performance and power prediction, multicore systems.