In recent years we have observed great advances in parallel platforms and the exponential growth of datasets in several domains. Undoubtedly, parallel programming is crucial to harness the performance potential of such platforms and to cope with very large datasets. However, quite often one has to deal with legacy software systems that may use third-party frameworks, libraries, or tools, and that may be executed in different multicore architectures. Managing different software configurations and adapt them for different needs is an arduous task, particularly when it has to be carried out by scientists or when dealing with irregular applications. In this paper, we present an approach to abstract legacy software systems using workflow modeling tools. We show how a basic pipeline is modeled and adapted—using model transformations—to different application scenarios, either to obtain better performance, or more reliable results. Moreover, we explain how the system we provide to support the approach is easily extensible to accommodate new tools and algorithms. We show how a pipeline of three irregular applications— all from phylogenetics—is mapped to parallel implementations. Our studies show that the derived programs neither downgrade performance nor sacrifice scalability, even in the presence of a set of asymmetric tasks and when using third-party tools.