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We consider a heterogeneous computing environment that consists of a collection of machines and task types. The machines vary in capabilities and different task types are better suited to specific machine architectures. We describe some of the difficulties with the current measures that are used to characterize heterogeneous computing environments and propose two new measures. These measures relate to the aggregate machine performance (relative to the given task types) and the degree of affinity that specific task types have to different machines. The latter measure of task-machine affinity is quantified using singular value decomposition. One motivation for using these new measures is to be able to represent a wider range of heterogeneous environments than is possible with previous techniques. An important application of studying the heterogeneity of heterogeneous systems is predicting the performance of different computing hardware for a given task type mix.