MeToo: Stochastic Modeling of Memory Traffic Timing Behavior

Abstract

The memory subsystem (memory controller, bus, andDRAM) is becoming a bottleneck in computer system performance. Optimizing the design of the multicore memory subsystem requires good understanding of the representative workload. A common practice in designing the memory subsystem is to rely on trace simulation. However, the conventional method of relying on traditional traces faces two major challenges. First, many software users are apprehensive about sharing their code (source or binaries) due to the proprietary nature of the code or secrecy of data, so representative traces are sometimes not available. Second, there is a feedback loop where memory performance affects processor performance, which in turnalters the timing of memory requests that reach the bus. Such feedback loop is difficult to capture with traces. In this paper, we present MeToo, a framework for generating synthetic memory traffic for memory subsystem design exploration. MeToo uses a small set of statistics that summarizes the performance behavior of the original applications, and generates synthetic traces or executables stochastically, allowing applications to remain proprietary. MeToo uses novel methods for mimicking the memory feedback loop. We validate MeToo clones, and show very good fit with the original applications' behavior, with an average error of only 4.2%, which is a small fraction of the errors obtained using geometric inter-arrival(commonly used in queueing models) and uniform inter-arrival.