Abstract : Many large-scale scientific applications require the processing of complete data sets made of individual data segments that can be manipulated independently following a single analysis procedure. Workflow managers have been designed for describing and controlling such complex application control flows. How- ever, when considering very data-intensive applications, there is a large poten- tial parallelism that should be properly exploited to ensure efficient processing. Distributed systems such as Grid infrastructures are promising for handling the load resulting from parallel data analysis and manipulation. Workflow managers can help in exploiting the infrastructure parallelism, given that they are able to handle the data flow resulting from the application's execution.