{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/16964"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/16964","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Data-race detection in transactions-everywhere parallel programming","abstract":"This thesis studies how to perform dynamic data-race detection in programs using \"transactions everywhere\", a new methodology for shared-memory parallel programming. Since the conventional definition of a data race does not make sense in the transactions-everywhere methodology, this thesis develops a new definition based on a weak assumption about the correctness of the target program's parallel-control flow, which is made in the same spirit as the assumption underlying the conventional definition. This thesis proves, via a reduction from the problem of 3cnf-formula satisfiability, that data-race detection in the transactions-everywhere methodology is an NP-complete problem. In view of this result, it presents an algorithm that approximately detects data races. The algorithm never reports false negatives. When a possible data race is detected, the algorithm outputs simple information that allows the programmer to efficiently resolve the root of the problem. The algorithm requires running time that is worst-case quadratic in the size of a graph representing all the scheduling constraints in the target program.","abstract_html":"This thesis studies how to perform dynamic data-race detection in programs using "transactions everywhere", a new methodology for shared-memory parallel programming. 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