Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption

TL;DR

This paper shows that selectively reducing compiler optimizations in -O2 can significantly improve performance and energy efficiency on embedded processors, with less testing effort than advanced techniques.

Contribution

It introduces a simple method to improve performance and energy savings by performing fewer optimizations in standard compiler levels, validated on ARM embedded processors.

Findings

Average execution time reduction of 2.4% and 5.3% on Cortex-M0 and M3

Performance improvements up to 90% over -O2

Achieves similar savings to advanced iterative and machine learning approaches

Abstract

This paper presents the interesting observation that by performing fewer of the optimizations available in a standard compiler optimization level such as -O2, while preserving their original ordering, significant savings can be achieved in both execution time and energy consumption. This observation has been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM Cortex-M3, using two different versions of the LLVM compilation framework; v3.8 and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated performance gains for at least half of the benchmarks for both processors. An average execution time reduction of 2.4% and 5.3% was achieved across all the benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with execution time improvements ranging from 1% up to 90% over the -O2. The savings that can be achieved are in the same range as what can be achieved by the state-of-the-art compilation approaches that use iterative compilation or machine learning to select flags or to determine phase orderings that result in more efficient code. In contrast to these time consuming and expensive to apply techniques, our approach only needs to test a limited number of optimization configurations, less than 64, to obtain similar or even better savings. Furthermore, our approach can support multi-criteria optimization as it targets execution time, energy consumption and code size at the same time.