EMPROF: Memory Profiling Via EM-Emanation in IoT and Hand-Held Devices

摘要

This paper presents EMPROF, a new method for profiling the performance impact of the memory subsystem without any support on, or interference with, the profiled system. Rather than rely on hardware support and/or software instrumentation on the profiled system, EMPROF analyzes the system's EM emanations to identify processor stalls that are associated with last-level cache (LLC) misses. This enables EMPROF to accurately pinpoint LLC misses in the execution timeline and to measure the cost (stall time) of each miss. Since EMPROF has zero "observer effect", so it can be used to profile applications that adjust their activity to their performance. It has no overhead on target machine, so it can be used for profiling embedded, hand-held, and IoT devices which usually have limited support for collecting, and limited resources for storing, the profiling data. Finally, since EMPROF can profile the system as-is, its profiling of boot code and other hard-to-profile software components is as accurate as its profiling of application code. To illustrate the effectiveness of EMPROF, we first validate its results using microbenchmarks with known memory behavior, and also on SPEC benchmarks running a cycle-accurate simulator that can provide detailed ground-truth data about LLC misses and processor stalls. We then demonstrate the effectiveness of EMPROF on real systems, including profiling of boot activity, show how its results can be attributed to the specific parts of the application code when that code is available, and provide additional insight on the statistics reported by EMPROF and how they are affected by the EM signal bandwidth provided to EMPROF.