Abstract
IceCube's simulation production relies largely on dynamic, heterogeneous resources spread around the world. Datasets consist of many thousands of job workflow subsets running in parallel as directed acyclic graphs (DAGs) and using varying resources. IceProd is a set of Python daemons which process job workflow and maintain configuration and status information on jobs before, during, and after processing. IceProd manages a complex workflow of DAGs to distribute jobs across all computing grids and optimize resource usage. IceProd2 is a new version of IceProd with substantial increases in security, reliability, scalability, and ease of use. It is undergoing testing and will be deployed this fall.
Highlights
IceProd has served the IceCube collaboration well over the last 8 years, processing Monte Carlo simulations, detector data, and analysis levels [1]
Datasets consist of many thousands of job workflow subsets running in parallel as directed acyclic graphs (DAGs) and using varying resources
IceProd manages a complex workflow of DAGs to distribute jobs across all computing grids and optimize resource usage
Summary
IceProd has served the IceCube collaboration well over the last 8 years, processing Monte Carlo simulations, detector data, and analysis levels [1]. It has run thousands of CPU-core years and stored over 2PB of data. It has experienced its share of issues and the initial software is running into fundamental design limitations. This article describes that history and the development of a second version of IceProd. The new version is a complete rewrite of the code base, fixing long-standing issues and preparing for the increased load of the few years
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