Abstract—JavaTMhas found widespread adoption across a va-riety of architectures. Understanding Java application behavior and further design and development of Java systems can be facil-itated by software based microarchitecture simulators. However, the use of cycle-accurate, user-mode, software

For simulation, a tradeoff exists between speed and accuracy. The more instructions simulated from the workload, the more accurate the results — but at a higher cost. To reduce processor simulation times, a variety of techniques have been introduced. Statistically sampled simulation is one method

This paper explores techniques for speeding up sampled microprocessor simulations by exploiting the observation that of the memory references that precede a sample, references that occur nearest to the sample are more likely to be germane during the sample itself. This means that accurately warming

Abstract — This paper proposes to speedup sampled microprocessor simulations by reducing warmup times without sacrificing simulation accuracy. It exploiting the observation that of the memory references that precede a sample cluster, references that occur nearest to the cluster are more likely

. Validation was performed using finiteelement simulation. The paper also introduces several effective methods for dynamic thermal management (DTM): “temperaturetracking” frequency scaling, localized toggling, and migrating computation to spare hardware units. Modeling temperature at the microarchitecture

the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure

This book describes the new generation of discrete choice meth-ods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling

presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound