Model-driven transformations for mapping parallel algorithms on parallel computing platforms
One of the important problems in parallel computing is the mapping of the parallel algorithm to the parallel computing platform. Hereby, for each parallel node the corresponding code for the parallel nodes must be implemented. For platforms with a limited number of processing nodes this can be done manually. However, in case the parallel computing platform consists of hundreds of thousands of processing nodes then the manual coding of the parallel algorithms becomes intractable and error-prone. Moreover, a change of the parallel computing platform requires considerable effort and time of coding. In this paper we present a model-driven approach for generating the code of selected parallel algorithms to be mapped on parallel computing platforms. We describe the required platform independent metamodel, and the model-to-model and the model-to-text transformation patterns. We illustrate our approach for the parallel matrix multiplication algorithm. Copyright © 2013 for the individual papers by the papers' authors.