SHF: Small: Towards a Versatile Analytical Modeling Toolset for Evaluating Memory Hierarchy Design

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Today, the primary tool used for evaluating future computer system designs is simulation, where each machine instruction and its impact on processor and memory state are simulated in great details. While powerful and versatile, processor simulation is exceedingly slow: roughly four to five orders of magnitude slower than native execution. In the future, solely relying on processor simulation will be too constraining, due to simultaneous increase in the complexity of hardware and software systems, and variety of workloads. Analytical modeling is an attempt to capture fundamental relationships between various parameters of a design and to capture how they affect performance or power/energy consumption. An analytical model is much faster to run and can be reasonably accurate. Unfortunately, widespread uses of analytical models have been hampered by the lack of widely available toolset that researchers can use and build upon, and the high barrier of entry into using and developing them. The objective of this proposal is to integrate various disparate analytical models into a single toolset that can be used by researchers for computer architecture design and evaluation. The expected outcome of this project is a tool that aids processor simulation through three important roles: providing ability to reason about what parameters determine the outcome of a performance phenomenon, discovering insights into how architecture parameters fundamentally relate to one another, and providing first-order approximation that allows narrowing down the search space for simulation studies. The project enables computer design and evaluation to be improved significantly in terms of quality (deeper insights can be obtained), as well as resource efficiency (less simulation time is required to arrive at the same observations). A deliverable of the project includes a toolset that integrates components of the project into a software package that is easy-to-use and has intelligent interface, and classroom materials suitable for undergraduate and graduate courses. These materials will be made available for public download, community enhancement, and community extension.