# STAR Data Reconstruction at NERSC/Cori, an adaptable Docker container approach for HPC

## TL;DR

This paper demonstrates how Docker containers can be used to adapt high-energy physics data reconstruction workflows for HPC systems like NERSC/Cori, addressing data transfer and deployment challenges.

## Contribution

It introduces a Docker-based approach for STAR data reconstruction on HPC, enabling scalable, efficient workflows comparable to traditional clusters.

## Key findings

- Efficient data transfer achieved over ESnet after endpoint optimization.
- Docker containers enable scalable deployment of conditions database.
- Workflow performance on Cori matches standard Linux clusters.

## Abstract

As HPC facilities grow their resources, adaptation of classic HEP/NP workflows becomes a need. Linux containers may very well offer a way to lower the bar to exploiting such resources and at the time, help collaboration to reach vast elastic resources on such facilities and address their massive current and future data processing challenges. In this proceeding, we showcase STAR data reconstruction workflow at Cori HPC system at NERSC. STAR software is packaged in a Docker image and runs at Cori in Shifter containers. We highlight two of the typical end-to-end optimization challenges for such pipelines: 1) data transfer rate which was carried over ESnet after optimizing end points and 2) scalable deployment of conditions database in an HPC environment. Our tests demonstrate equally efficient data processing workflows on Cori/HPC, comparable to standard Linux clusters.