MEMST: Cloning Memory Behavior using Stochastic Traces

Abstract

Memory Controller and DRAM architecture are critical aspects of Chip Multi Processor (CMP) design. A good design needs an in-depth understanding of end-user workloads. However, designers rarely get insights into end-user workloads because of the proprietary nature of source code or data. Workload cloning is an emerging approach that can bridge this gap by creating a proxy for the proprietary workload (clone). Cloning involves profiling workloads to glean key statistics and then generating a clone offline for use in the design environment. However, there are no existing cloning techniques for accurately capturing memory controller and DRAM behavior that can be used by designers for a wide design space exploration. We propose Memory EMulation using Stochastic Traces, MEMST, a highly accurate black box cloning framework for capturing DRAM and MC behavior. We provide a detailed analysis of statistics that are necessary to model a workload accurately. We will also show how a clone can be generated from these statistics using a novel stochastic method. Finally, we will validate our framework across a wide design space by varying DRAM organization, address mapping, DRAM frequency, page policy, scheduling policy, input bus bandwidth, chipset latency, DRAM die revision, DRAM generation and DRAM refresh policy. We evaluated MEMST using CPU2006, BioBench, Stream and PARSEC benchmark suites across the design space for single-core, dual-core, quad-core and octa-core CMPs. We measured both performance and power metrics for the original workload and clones. The clones show a very high degree of correlation with the original workload for over 7900 data points with an average error of 1.8% and 1.6% for transaction latency and DRAM power respectively.