<?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>Book Chapter</dc:product_type><dc:title>Hidden Assumptions in Static Verification of Data-race Free GPU Programs</dc:title><dc:creator>Cogumbreiro, Tiago (ORCID:0000000232099258); Lange, Julien (ORCID:0000000196971378)</dc:creator><dc:corporate_author/><dc:editor/><dc:description>GPUs are massively parallel devices that promise a great return of investment at a cost: GPUs are notably difficult to get right. We discuss a static analysis tool for GPU programs, called Faial, that can detect data-races and data-race freedom. We studied a dataset of 191 data-race free programs and found that 98% needs specific thread configuration to be analyzable, and that 27% needs user-provided assertions to be analyzable. We also report that Faial was able to find data-races in at least 92% of the kernels with missing assumptions.</dc:description><dc:publisher>Springer Nature Switzerland</dc:publisher><dc:date>2025-01-01</dc:date><dc:nsf_par_id>10668319</dc:nsf_par_id><dc:journal_name/><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>55 to 63</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1007/978-3-031-97492-2_6</dc:doi><dcq:identifierAwardId>2204986</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>