  Many scientific workloads are comprised of many tasks, where each task is an
independent simulation or analysis of data. The execution of millions of tasks
on heterogeneous HPC platforms requires scalable dynamic resource management
and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the
Pilot abstraction, addresses these challenges and serves as an effective
runtime system to execute workloads comprised of many tasks. In this paper, we
characterize the performance of executing many tasks using RP when interfaced
with JSM and PRRTE on Summit: RP is responsible for resource management and
task scheduling on acquired resource; JSM or PRRTE enact the placement of
launching of scheduled tasks. Our experiments provide lower bounds on the
performance of RP when integrated with JSM and PRRTE. Specifically, for
workloads comprised of homogeneous single-core, 15 minutes-long tasks we find
that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are
negligible; and PRRTE supports optimizations that lower the impact of overheads
and enable resource utilization of 63% when executing O(16K), 1-core tasks over
404 compute nodes.
