High Performance Distributed Computing is essential to boost scientific progress in many areas of science and to efficiently deploy a number of complex scientific applications. These applications have different characteristics that require distinct computational resources too. In this work we propose a systematic performance evaluation methodology. The focus of our methodology begins on scientific application characteristics, and then considers how these characteristics interact with the problem size, with the programming language and finally with a specific computational architecture. The computational experiments developed highlight this model of evaluation and indicate that optimal performance is found when we evaluate a combination of application class, program language, problem size and architecture model.