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oai:www.ideals.illinois.edu:2142/81985 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Padua, David A. Cascaval, Gheorghe Calin 2015-09-25T20:21:18Z 2015-09-25T20:21:18Z 2000 2000 We use stack distances to quantify locality and we show that the average locality computed using stack distances is a very reliable metric. A new algorithm for stack processing, that is 30% faster than the best know algorithm on the suite of programs traced, is also presented. Made available in DSpace on 2015-09-25T20:21:18Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9989955.pdf: 5612324 bytes, checksum: 2b3ee6aaab528e67b63da61d6363477b (MD5) Previous issue date: 2000 Embargo set by: Seth Robbins for item 83266 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Open Restriction set for Item 83266 on 2018-04-17T14:42:18Z with date null by nrhodes3@illinois.edu. Open Restriction set for Item 83266 on 2018-04-17T14:42:21Z with date null by nrhodes3@illinois.edu. Open 124 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000. http://hdl.handle.net/2142/81985 (MiAaPQ)AAI9989955 eng Computer Science Compile -Time Performance Prediction of Scientific Programs text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.