Multidimensional scaling (MDS) is a technique for visualizing multidimensional data points as a 2D scatter plot. It can be applied to execution profiles of software to reveal how similar executions are to one another. This is useful for certain software engineering applications, which require accurate representation of small dissimilarities and nearest neighbor relationships.. However, the high-dimensionality of profiles can cause MDS techniques to represent small dissimilarities poorly. We evaluate several variants of MDS on large sets of profiles, to see which techniques produce the most accurate displays. These include four previously proposed techniques — classical scaling followed by iterative majorization, energy minimization, ordinal MDS, and cluster differences scaling — and two techniques of our invention — hierarchical MDS and sparse region scaling. The results suggest that each technique except ordinal MDS can significantly improve the representation of small dissimilarities between program executions and that hierarchical MDS and sparse region scaling perform best overall.