  Many extreme scale scientific applications have workloads comprised of a
large number of individual high-performance tasks. The Pilot abstraction
decouples workload specification, resource management, and task execution via
job placeholders and late-binding. As such, suitable implementations of the
Pilot abstraction can support the collective execution of large number of tasks
on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and
extensible Python-based Pilot system. We describe RP's design, architecture and
implementation. We characterize its performance and show its ability to
scalably execute workloads comprised of thousands of MPI tasks on Titan--a DOE
leadership class facility. Specifically, we investigate RP's weak (strong)
scaling properties up to 131K (65K) cores and 4096 (16384) 32 core tasks.
RADICAL-Pilot can be used stand-alone, as well as integrated with other tools
as a runtime system.
