Generic Processor Modeling for Automatically Generating Very Fast Cycle-Accurate Simulators

Abstract

Detailed modeling of processors is required for validating processor behavior and evaluating parameters such as performance and power consumption. Fast cycle-accurate simulators are essential in handling today's complex hardware and software designs at a reasonable time. These problems are challenging enough by themselves and have seen many previous research efforts. Addressing both simultaneously is even more challenging, with many existing approaches focusing on one over another. Abstract models in fast simulators do not provide enough information required for different phases of the design. On the other hand, detailed models are very difficult to generate and result in very slow simulators. In this paper, a modeling approach based on reduced colored Petri net (RCPN) is proposed, which has the following three advantages: 1) it is very generic and support a wide range of processor features; 2) it offers a very simple and intuitive yet formal way of modeling pipelined processors; and 3) it can generate high-performance cycle-accurate simulators. RCPN inherits all useful features of colored Petri nets while avoiding their exponential growth in complexity. In this paper, it is shown how this approach is general enough to model features such as very long instruction word out-of-order execution, dynamic scheduling, register renaming, hazard detection, and branch prediction. Furthermore, the results of generating cycle-accurate simulators from RCPN models of XScale and StrongArm processors are shown, where an order of magnitude (~15 times on the average) speedup over the popular SimpleScalar advanced reduced instruction set computing machine simulator is achieved