Improving the speed vs. accuracy tradeoff for simulating shared-memory multiprocessors with ILP processors
Durbhakula, Suryanarayana N. Murthy
Adve, Sarita V.
Master of Science thesis
Current simulators for shared-memory multiprocessor architectures involve a large tradeoff between simulation speed and accuracy. Most simulators assume much simpler processors than the current generation of processors that aggressively exploit instruction-level parallelism (ILP). This can result in large simulation inaccuracies. A few newer simulators model current ILP processors more accurately, but are about ten times slower. This study proposes and evaluates a new simulation technique that requires almost no compromise in accuracy and far less compromise in speed compared to the state-of-the-art. This technique uses a novel adaptation of direct execution, a methodology used widely for simulation of multiprocessors with simple processors. We develop a new simulator based on this technique, called DirectRSIM. We compare the performance and accuracy of DirectRSIM with three other simulators--two current direct execution simulators that use a simple processor model, and RSIM, a state-of-the-art detailed simulator for multiprocessors with ILP processors. For various combinations of applications and system configurations, we find that DirectRSIM is an average of 4 times faster than RSIM with an average relative error of 1.6%. In contrast, the current direct execution simulators see large and variable errors relative to RSIM, with an average of around 40% with the best methodology and 130% for the most commonly used methodology. Despite its superior accuracy, DirectRSIM achieves a speed within a factor of 2.7 of that achieved by the current direct execution simulators with simple processors. Although the performance advantage of simple processor based simulators is still significant, it may no longer be enough to justify the errors that such simulators see in modeling the performance of shared-memory systems with state-of-the-art processors.
Engineering, Electronics and Electrical; Computer Science