An analysis of address and branch patterns with PatternFinder

Abstract

Current processors employ aggressive prediction mechanisms to improve performance and reduce power. It is increasingly important to understand and quantify a program's dynamic behavior to effectively design next-generation prediction mechanisms. In this paper, we develop algorithms and mechanisms inspired by DNA discovery tools to analyze and quantify program dynamic behavior in terms of regularities and patterns. We describe our PatternFinder tool and analyze its results to summarize most important branch and data address pattern behaviors for a set of program traces and SPEC CPU 2006 benchmarks. M aking the common case fast is a design principle that has been used in microprocessor design for decades. This principle applies when determining how to spend resources, since the performance impact on making some occurrence faster is higher if the occurrence is frequent. Although quantifying frequent behavior in an application's dynamic execution behavior is trivial in cases such as observing the frequency of each type of instruction, it is very challenging to summarize dynamic data reference behavior (1). As a finding overlapping or approximate patterns. In this paper, inspired by DNA discovery tools (5, 6), we adopt and revise the methods motivated by suffix trees (5) in order to develop comprehensive pattern discovery tools targeted for computer architecture. Suffix trees have several advantages over SEQUITUR in designing such a tool, which we discuss in Section 3. In this paper, we present a novel pattern analysis tool, the PatternFinder, which is designed to discover and quantify patterns, and the results produced by the tool that quantify data address and branch patterns in dynamic program behavior. PatternFinder is an offline analysis tool that can summarize input sequences using patterns, quantify spatial and temporal regularities in input sequences and enumerate hot patterns based on specified scoring criteria (e.g., coverage). It allows user customizable search with a number of input parameters. PatternFinder can be used to gain insights into hardware and software optimization opportunities as well as perform pattern-centric