Evaluating the memory system behavior of smartphone workloads

Modern smartphones comprise several processing and input/output units that communicate mostly through main memory. As a result, memory represents a critical performance bottleneck for smartphones. This work¹ introduces a set of emerging workloads for smartphones and characterizes the performance of several memory controller policies and address-mapping schemes for those workloads. The workloads include high-resolution video conferencing, computer vision algorithms such as upper-body detection and feature extraction, computational photography techniques such as high dynamic range imaging, and web browsing. This work also considers combinations of these workloads that represent possible use cases of future smartphones such as detecting and focusing on people or other objects in live video. While some of these workloads have been characterized before, this is the first work that studies address mapping and memory controller scheduling for these workloads. Experimental analysis demonstrates: (1) Most of the workloads are either memory throughput or latency bound straining a conventional smartphone main memory system. (2) The address mapping schemes that balance row locality with concurrency among different banks and ranks are best. (3) The FR-FCFS with write drain memory scheduler performs best, outperforming some more recently proposed schedulers targeted at multi-threaded workloads on general purpose processors. These results suggest that there is potential to improve memory performance and that existing schedulers developed for other platforms ought to be revisited and tuned to match the demands of such smartphone workloads.