Discrete Event Simulations by Aitor Goti

By Aitor Goti

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Cache simulation results. 3 Branch Prediction Algorithm Comparison Experiment This experiment was conducted to demonstrate the capability of using external modules with the framework. The framework along with a preconfigured MIPS64 simulation was given to a group of graduate computer architecture students to produce external branch A dynamically configurable discrete event simulation framework for many-core chip multiprocessors 29 predictor modules. Each student was provided with the source code for a simple two-bit branch predictor and was tasked with creating a two-level correlating predictor and a tournament predictor.

Furthermore, based on current methods, deriving the state-space representation is performed manually and ad-hoc, as no systematic and unified method is available. In the light of these difficulties, the following sections propose a systematic framework for deriving the state-space representation. The target systems are allowed to have capacity constraints within single and between facilities, and moreover have varying processing times for each job. 4. Considering the Capacity Constraints We extend the conventional state-space representation in Dioid algebra, and derive a systematic framework for modelling a class of repetitive systems with capacity constraints.

However, we should note here that these rules are defined exclusively for operators  and \ , not for + and -. For multiple numbers, if xi  R max , we simply denote: l x k 1 mn k nl  x1  x2    xl For matrices X , Y  R max and Z  R max , we define the following two operations in analogy to the  and  operations. Modelling methods based on discrete algebraic systems 43 l [ X  Y ]ij  min([X ]ij , [Y ]ij ) , [ X  Z ]ij  ([ X ] k 1 ik \ [ Z ]kj )  min ( [ X ]ik  [ Z ]kj ) k 1,,l For simplicity, several references adopt a different definition for operator  , where X  Z gives the same result as X T  Z based on the above definition.

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