Modeling performance and power matrix of disparate computer systems using machine learning techniques modeling compiler systems selection

Abstract

quotIn the last couple of decades, there has been an exponential growth in the processor, cache, and memory features of computer systems. These hardware features play a vital role in determining the performance and power of a software application when executed on different computer systems. Furthermore, any minor alterations in hardware features or applications can alter and impact the performance and power consumption. Compute-intensive (compute-bound) applications have a higher dependence on processor features, while data-intensive (memory-bound) applications have a higher dependence on memory features. To match the customized budgets in performance and power, selecting computer systems with appropriate hardware features (processor, cache, and memory) becomes extremely essential. To adhere to user-specific budgets, selecting computer systems requires access to physical systems to gather performance and power utilization data. To expect a user to have access to physical systems to achieve this task is prohibitive in cost; therefore, it becomes essential to develop a virtual model which would obviate the need for physical systems. Researchers have used system-level simulators for decades to build simulated computer systems using processor, cache, and memory features to provide estimates of performance and power. In one approach, building virtual systems using a full-system simulator (FSS), provides the closest possible estimate of performance and power measurement to a physical system. In the recent past, machine learning algorithms have been trained on the above-mentioned accurate FSS models to predict performance and power for varying features in similar systems, achieving fairly accurate results. However, building multiple computer systems in a full-system simulator is complex and an extremely slow process. The problem gets compounded due to the fact that access to such accurate simulators is limited. However, there is an alternative approach of utilizing the open-source gem5 simulator using its emulation...