Sampling Simulation through Program Characteristic Analysis for Modern Multicore Architectures
Performance evaluation through software simulation to explore hardware architecture tradeoffs is a critical component of the complete design flow. With increasing number of processing units and the growth of complexity of each unit, today’s system has grown rapidly in intricacy. The time needed to simulating these modern systems using software will also grow rapidly and will soon become prohibitively slow. This thesis proposes an adaptive adjustment sampling strategy utilizing program characteristic to accelerate simulation speed. Moreover, the adaptive adjustment strategy presented also improves the simulation accuracy by taking into consideration for program behaviors. In our proposed scheme, we first apply statistical sampling method to find suitable program simulation sampling parameters. We, then, by observing the dynamic behavior of each application collect traces with less overhead to do effective sampling adjustment. We not only utilize this proposed sampling approach for simulating conventional multicore CPU architecture, we also build a prototype of sampling simulation infrastructure for GPU architecture. This is of good value because we believe GPU more than CPU, having more and more threads, will exasperate the issue of simulation performance degradation now and in the future.