1. Introduction

RADICAL-Pilot (RP) is a Pilot Job system written in Python. It allows a user to run large numbers of computational tasks (called ComputeUnits) concurrently on one or more remote ComputePilots that RADICAL-Pilot can start transparently on a multitude of different distributed resources, like HPC clusters and Clouds.

In this model, a part (slice) of a resource is acquired by a user’s application so that the application can directly schedule ComputeUnits into that resource slice, rather than going through the system’s job scheduler. In many cases, this can drastically shorten overall exeuction time as the individual ComputeUnits don’t have to wait in the system’s scheduler queue but can execute directly on the ComputePilots.

ComputeUnits are often single-core / multi-threaded executables, but RADICAL-Pilot also supports execution of parallel executables, for example based on MPI, OpenMP, YARN/HADOOP and Spark.

RADICAL-Pilot is not a static system, but it rather provides the user with a programming library (“Pilot-API”) that provides abstractions for resource access and task management. With this library, the user can develop everything from simple “submission scripts” to arbitrarily complex applications, higher- level services and tools.


The RP architecture overview image above shows the main components or RP, and their functional relationships. The RP system will interpret pilot descriptions and submit the respective pilot instances on the target resources. It will then accept unit descriptions and submit those for execution onto the earlier created pilots. The Chapter RADICAL-Pilot Overview will discuss those concepts in some more detail, before then turning to Installation which will describe how RP is deployed and configured, so that the reader can follow the upcoming examples.