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In this paper we present a new methodology for the performance prediction of parallel programs on parallel platforms ranging from shared-memory to distributed-memory (vector) machines. The methodology comprises a procedural program and machine specification paradigm based on PAMELA (PerformAnce ModEling LAnguage), along with a performance calculus, called “serialization analysis”. This calculus extends conventional parallel program analysis technology by explicitly accounting for resource contention, yet at the low evaluation cost typical for static techniques. It is shown that, where conventional techniques introduce fundamental errors, predictions from serialization analysis remain realistic. Apart from the merits of the methodology itself, this high reliability/cost ratio makes PAMELA an attractive candidate for compile-time application within the performance prediction hierarchy often found in parallel programming environments.