{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:05:35Z","timestamp":1778771135899,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000001","name":"Collaborative Research: Global Pervasive Computational Epidemiology","doi-asserted-by":"publisher","award":["1918626"],"award-info":[{"award-number":["1918626"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"AI Institute for Cosmic Origins","doi-asserted-by":"publisher","award":["2421782"],"award-info":[{"award-number":["2421782"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"CyberTraining for Students and Technologies from Generation Z","doi-asserted-by":"publisher","award":["2200409"],"award-info":[{"award-number":["2200409"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"RINAS: Data I\/O CyberInfrastructure for Extreme-scale Foundation Model and Generative AI Training on HPC","doi-asserted-by":"publisher","award":["2504401"],"award-info":[{"award-number":["2504401"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of High Performance Computing Applications"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>\n                    Large-scale astronomical image data processing and prediction are essential for astronomers, providing crucial insights into celestial objects, the universe\u2019s history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. Using redshift prediction with the AstroMAE model demonstrated CAI\u2019s scalability and efficiency, achieving inference on a 12.6\u00a0GB dataset in only 28 seconds compared to 140.8 seconds on HPC GPUs and 1793 seconds on HPC CPUs. CAI also achieved significantly higher throughput, reaching 18.04 billion bits per second (bps), and maintained near-constant inference times as data sizes increased, all at minimal computational cost (under $5 per experiment). We also process large-scale data up to 1\u00a0TB to show CAI\u2019s effectiveness at scale. CAI thus provides a highly scalable, accessible, and cost-effective inference solution for the astronomy community. The code is accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/UVA-MLSys\/AI-for-Astronomy\">https:\/\/github.com\/UVA-MLSys\/AI-for-Astronomy<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/10943420251399942","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T06:29:32Z","timestamp":1764311372000},"page":"352-366","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Scalable cosmic AI inference using cloud serverless computing"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3653-4373","authenticated-orcid":false,"given":"Mills","family":"Staylor","sequence":"first","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2165-2932","authenticated-orcid":false,"given":"Amirreza","family":"Dolatpour Fathkouhi","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2894-8584","authenticated-orcid":false,"given":"Md Khairul","family":"Islam","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5119-742X","authenticated-orcid":false,"given":"Kaleigh","family":"O\u2019Hara","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1024-7708","authenticated-orcid":false,"given":"Ryan Ghiles","family":"Goudjil","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1017-1391","authenticated-orcid":false,"given":"Geoffrey","family":"Fox","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8198-4117","authenticated-orcid":false,"given":"Judy","family":"Fox","sequence":"additional","affiliation":[{"name":"University of Virginia"},{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0067-0049\/193\/2\/29"},{"key":"e_1_3_3_3_1","unstructured":"AWS (2024) Serverless. https:\/\/aws.amazon.com\/serverless"},{"key":"e_1_3_3_4_1","volume-title":"FMI: The FaaS message interface","author":"B\u00f6hringer R","year":"2022","unstructured":"B\u00f6hringer R, Copik M, Calotoiu A, et al. 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