Simulation is a vital tool used by architects to develop new architectures. However, because of the complexity of modern architectures and the length of recent benchmarks, detailed simulation of programs can take extremely long times. This impedes the exploration of processor design space which the architects need to do to find the optimal configuration of processor parameters. Sampling is one technique which reduces the simulation time without adversely affecting the accuracy of the results. Yet, most sampling techniques either ignore the warm-up issue or require significant development effort on the part of the user.In this thesis we tackle the problem of reconciling state-of-the-art warm-up techniques and the latest sampling mechanisms with the triple objective of keeping the user effort minimum, achieving good accuracy and being agnostic to software and hardware changes. We show that both the representative and statistical sampling techniques can be adapted to use warm-up mechanisms which can accommodate the underlying architecture's warm-up requirements on-the-fly. We present the experimental results which show an accuracy and speed comparable to latest research. Also, we leverage statistical calculations to provide an estimate of the robustness of the final results.
