Cache behavior of a program has an ever-growing strong impact on its execution time. Characterizing the behavior by visible patterns is considered a way to pinpoint the bottleneck against performance.
This paper presents a framework of visualization for trace distributions to extract the useful cache behavior patterns. We focus on cache misses, reuse distances, temporal or spatial localities, etc. The histograms of the distribution patterns measure the behavior in quantity, revealing effective program optimizations. The performance bottlenecks are exposed as hot spots highlighted in the source code, showing the exact location to apply suitable optimizations. The impact of the source-level program optimizations, again, can be verified by the visualization tool.