{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:49:32Z","timestamp":1776491372595,"version":"3.51.2"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100014801","name":"Ankara Y\u0131ld\u0131r\u0131m Beyaz\u0131t University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014801","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Monte Carlo integration (MCI) is widely used for evaluating high-dimensional or non-analytic functions, but its large number of function evaluations can make it computationally demanding. As modern scientific applications increasingly rely on GPU acceleration, balancing numerical accuracy and performance has become a key challenge. To address this, we present a GPU-accelerated mixed-precision MCI framework that adaptively selects precision based on local numerical behavior using heuristics derived from gradient, variance, and average value analysis. Two precision allocation strategies are explored: term-wise and region-wise, both implemented with CUDA batch processing to maximize GPU efficiency. Experimental results on representative test functions of varying dimensionality demonstrate speedups of up to 4.9\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times $$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    compared to full double precision, while maintaining controlled relative error. The proposed framework achieves a practical balance between computational efficiency and numerical reliability. It allows integration tasks on GPUs to run accurately and to adapt their precision automatically for higher performance.\n                  <\/jats:p>","DOI":"10.1007\/s00607-026-01656-7","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T03:48:58Z","timestamp":1775447338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive mixed-precision Monte Carlo integration on GPUs"],"prefix":"10.1007","volume":"108","author":[{"given":"Ferhat Onur","family":"\u00d6zgan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berke","family":"Kabasakal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahreddin \u015e\u00fckr\u00fc","family":"Torun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"issue":"2","key":"1656_CR1","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/S0885-064X(03)00003-7","volume":"19","author":"X Wang","year":"2003","unstructured":"Wang X, Fang K-T (2003) The effective dimension and quasi-Monte Carlo integration. 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