  Workflows applications are becoming increasingly important to support
scientific discovery. That is leading to a proliferation of workflow management
systems and, thus, to a fragmented software ecosystem. Integration among
existing workflow tools can improve development efficiency and, ultimately,
increase the sustainability of scientific workflow software. We describe our
experience with integrating RADICAL-Pilot (RP) and Parsl as a way to enable
users to develop and execute workflow applications with heterogeneous tasks on
heterogeneous high-performance computing resources. We describe our approach to
the integration of the two systems and detail the development of RPEX, a Parsl
executor which uses RP as its workload manager. We develop an RP executor that
executes heterogeneous MPI Python functions on CPU cores and GPUs. We measure
the weak and strong scaling of RPEX, RP, and Parsl when providing new
capabilities to two paradigmatic use cases: Colmena and Ice Wedge Polygons
