<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>A GPU-Accelerated AMR Solver for Gravitational Wave Propagation</dc:title><dc:creator>Fernando, Milinda; Neilsen, David; Hirschmann, Eric; Zlochower, Yosef; Sundar, Hari; Ghattas, Omar; Biros, George</dc:creator><dc:corporate_author/><dc:editor/><dc:description>Simulations to calculate a single gravitational waveform (GW) can take several weeks. Yet, thousands of such simulations are needed for the detection and interpretation of gravitational waves. Future detectors will require even more accurate waveforms than those currently used. We present here the first large scale, adaptive mesh, multi-GPU numerical relativity (NR) code together with performance analysis and benchmarking. While comparisons are difficult to make, our GPU extension of the Dendro-GR NR code achieves a 6x speedup over existing state-of-the-art codes. We achieve 800 GFlops/s on a single NVIDIA A100 GPU with an overall 2.5x speedup over a two-socket, 128-core AMD EPYC 7763 CPU node with an equivalent CPU implementation. We present detailed performance analyses, parallel scalability results, and accuracy assessments for GWs computed for mass ratios q=1,2,4. We also present strong scalability up to 8 A100s and weak scaling up to 229,376 ×86 cores on the Texas Advanced Computing Center's Frontera system.</dc:description><dc:publisher/><dc:date>2022-11-01</dc:date><dc:nsf_par_id>10426126</dc:nsf_par_id><dc:journal_name>SC22: International Conference for High Performance Computing, Networking, Storage and Analysis</dc:journal_name><dc:journal_volume/><dc:journal_issue>Article No.: 75</dc:journal_issue><dc:page_range_or_elocation>1 to 15</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/SC41404.2022.00080</dc:doi><dcq:identifierAwardId>2004044; 2110338; 2018420; 1912930; 1912883</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>