Visualizing dynamic metrics with profiling blueprints

Abstract

While traditional approaches to code profiling help locate performance bottlenecks, they offer only limited support for removing these bottlenecks. The main reason is the lack of visual and detailed runtime information to identify and eliminate computation redundancy. We provide two profiling blueprints which help identify and remove performance bottlenecks. The structural distribution blueprint graphically represents the CPU consumption share for each method and class of an application. The behavioral distribution blueprint depicts the distribution of CPU consumption along method invocations, and hints at method candidates for caching optimizations. These two blueprints helped us to significantly optimize Mondrian, an open source visualization engine. Our implementation is freely available for the Pharo development environment and has been evaluated in a number of different scenarios. © 2010 Springer-Verlag.