Improving ALMA's data processing effciency using a holistic approach

TL;DR

This paper discusses ALMA's efforts to enhance data processing efficiency through a comprehensive approach, addressing operational challenges, optimization strategies, and future plans to handle increasing data volumes effectively.

Contribution

It introduces a holistic methodology for improving ALMA's data processing efficiency, including operational risk mitigation and strategic optimization initiatives.

Findings

Implemented strategies reduced processing backlog

Optimized workflows increased data throughput

Future plans aim to process 80% of datasets with fewer staff

Abstract

ALMA (Atacama Large Millimeter/submillimeter Array) is the world's largest ground-based facility for observations in the millimeter/submillimeter regime. One of ALMA's outstanding characteristics is the large effort dedicated to the quality assurance (QA) of the calibrated and imaged data products offered to the astronomical community. The Data Management Group (DMG), in charge of the data processing, review, and delivery of the ALMA data, consists of approximately 60 experts in data reduction, from the ALMA Regional Centers (ARCs) and the Joint ALMA Observatory (JAO), distributed in fourteen countries. With a throughput of more than 3,000 datasets per year, meeting the goal of delivering the pipeline-able data products within 30 days after data acquisition is a huge challenge. This paper presents (a) the history of data processing at ALMA, (b) the challenges our team had and is still facing, (c) the methodology followed to mitigate the operational risks, (d) the ongoing optimization initiatives, (e) the current data processing status, (f) the strategy which is being followed so that, in a few Cycles from now, a team of approximately 10 data reducers (DRs) at JAO can process and review some 80% of the datasets collected during an observing cycle, and, finally, (g) the important role of the ARCs for processing the remaining datasets.