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Runs of a benchmark or a suite of benchmarks are inadequate either to characterize a given machine or to predict the running time of some benchmark not included in the suite. Further, the observed results are quite sensitive to the nature of the benchmarks, and the relative performance of two machines can vary greatly depending on the benchmarks used. In this dissertation we propose and investigate a new approach to CPU performance evaluation. The main idea is to represent machine performance and program execution in terms of a high level abstract machine model. The model is machine-independent and thus is valid on any uniprocessor. We have developed tools to measure the performance of a variety of machines, from workstations to supercomputers. We have also characterized the execution of many large applications, including the SPEC and Perfect benchmark suites. By merging these machine and program characterizations, we can estimate execution times quite accurately for arbitrary machine-program combinations Another contribution of this dissertation is to propose and investigate new metrics for machine and program similarity and the information that can be derived from them. We define the concept of pershapes, which represent the level of performance distributions. This metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used. A similar metric for programs allows us to compare and cluster them according to their dynamic behavior. All this information helps to identify those parameters in machines and programs which are the most important in determining their execution times. Further, it provides a way for designers and users to identify potential bottlenecks in machines, compilers, and applications.