Abstract
Performance is an important aspect of software quality; in some real-time systems, poor performance can cause physical damage or even deaths. This paper describes how data from profiles taken at different loads may be combined to help locate performance bottlenecks that are distributed widely throughout a large program or system, such as those due to inlined functions or macros. This paper also describes how this technique may be used to pinpoint several types of performance bottlenecks in large programs running on shared-memory multiprocessors. In this environment, the critical bottleneck might consume only a small fraction of the total resources (due to Amdahl's law) and might be widely distributed throughout the program under test. Such a bottleneck can be very difficult to find when using traditional profiling techniques. >
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