Novel Framework for Designing Representative Usage-Based Benchmarks for Smartphones

Abstract

Smartphones dominate the consumer electronics market. Heterogeneous multicore processors are nowadays often integrated into smartphones. The power and performance evaluation of mobile multicore processors is a key issue in their design. This paper is the first to address the issue of improving the power and performance in modern, multicore smartphones by revisiting existing benchmarks used for evaluating these devices. Unlike earlier computers, modern consumer devices execute workloads that are characterized by heterogeneous concurrently running processes, which are launched at different times. Yet, modern smartphones architecture designs are still being evaluated using representative benchmark suites comprised of individual programs. This method of evaluation can be very misleading, as it does not represent the way these devices are actually being used. This paper first proposes a novel learning framework that identifies representative usage patterns of various real workloads, where each representative pattern represents a single class in a heterogeneous collection of actual usage patterns. We then use these representative patterns to optimize the power and performance of common multicore computer architectures integrated into smartphones. Our experiments show that using our representative patterns for optimization results in, on average , 11.5% and 16.7% power and performance improvements, respectively, compared to the state-of-art optimization methods.