Enhancing Multiprocessor Architecture Simulation Speed Using Matched-Pair Comparison

While cycle-level, full-system architecture simulation tools are capable of estimating performance at arbitrary accuracy, the time to simulate an entire application is usually prohibitive. Moreover, simulating multi-threaded applications further exacerbates this problem as most simulation tools are single-threaded. Recently, statistical sampling techniques, such as SMARTS, have managed to bring down the simulation time significantly by making it possible to only simulate about 1% of the code with sufficient accuracy. However, thousands of simulation points throughout the benchmark must still be simulated. First of all, we propose to use the well-established statistical method matched-pair comparison and motivate why this will bring down the number of simulation points needed to achieve a given accuracy. We apply it to single-processor as well as multiprocessor simulation and show that it is capable of reducing the number of needed simulation points by one order of magnitude. Secondly, since we apply the technique to single- as well as multiprocessors, we study for the first time the efficiency of statistical sampling techniques in multiprocessor systems to establish a baseline to compare with. We show theoretically and confirm experimentally, that while the instruction throughput vary significantly on each individual processor, the variability of instruction throughput across processors in a multiprocessor system decreases as we increase the number of processors for some important workloads. Thus, a factor of P fewer simulation points, where P is the number of processors, are needed to begin with when sampling is applied to multiprocessors