Workload characterization supporting the development of domain-specific compiler optimizations using decision trees for data mining

Abstract

Embedded systems have successfully entered a broad variety of application domains such as automotive and industrial control, telecommunications, networking, digital media, consumer equipment, office automation and many more. In this paper we investigate if there exist any fundamental differences between application domains that justify the development and tuning of domain-specific compilers. We develop an automated approach that is capable of identifying domain-specific workload characterizations and presenting them in a readily interpretable format based on decision trees. The generated workload profiles summarize key resource utilization issues and enable compiler engineers to address the highlighted bottlenecks. We have evaluated our methodology against the industrial EEMBC benchmark suite and three popular embedded processors and have found that workload profiles differ significantly between application domains. We demonstrate that these characteristics can be exploited for the development of domain-specific compiler optimizations. In a case study we show average performance improvements of up to 44% for a class of networking applications.