{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:56:23Z","timestamp":1777676183694,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2002,11,1]],"date-time":"2002-11-01T00:00:00Z","timestamp":1036108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of High Performance Computing Applications"],"published-print":{"date-parts":[[2002,11]]},"abstract":"<jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Lack of effective performance-evaluation environments is a major barrier to the broader use of high performance computing. Conventional performance environments are based on profiling and event instrumentation. It becomes problematic as parallel systems scale to hundreds of nodes and beyond. A framework of developing an integrated performance modeling and prediction system, SCALability Analyzer (SCALA), is presented in this study. In contrast to existing performance tools, the program performance model generated by SCALA is based on scalability analysis. SCALA assumes the availability of modern compiler technology, adopts statistical and symbolic methodologies, and has the support of browser interface. These technologies, together with anew approach of scalability analysis, enable SCALA to provide the user with a more intuitive level of performance analysis for scalable computing. A prototype SCALA system has been implemented. Initial experimental results show that SCALA is unique in its ability of revealing the scaling properties of a computing system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/109434200201600401","type":"journal-article","created":{"date-parts":[[2017,7,14]],"date-time":"2017-07-14T16:52:15Z","timestamp":1500051135000},"page":"357-370","source":"Crossref","is-referenced-by-count":0,"title":["Scala: A Performance System for Scalable Computing"],"prefix":"10.1177","volume":"16","author":[{"given":"Xian-He","family":"Sun","sequence":"first","affiliation":[{"name":"Department Of Computer Science, Illinois Institute Of Technology, Chicago, Il 60616, Usa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Fahringer","sequence":"additional","affiliation":[{"name":"Institute For Software Technology And Parallel Systems, University Of Vienna Liechtensteinstr. 22 1090 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Pantano","sequence":"additional","affiliation":[{"name":"Institute For Software Technology And Parallel Systems, University Of Vienna Liechtensteinstr. 22 1090 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2002,11,1]]},"reference":[{"key":"bibr1-109434200201600401","doi-asserted-by":"crossref","unstructured":"Adve V.S., Crummey J.M., Anderson M., Kennedy K., Wang J.C., and Reed D.A. 1995. \u201cAn integrated compilation performance analysis environment for data parallel pro-grams,\u201d in Proc. of Supercomputing, (San Diego, CA) December.","DOI":"10.1145\/224170.224340"},{"key":"bibr2-109434200201600401","unstructured":"Aydt R. 1995. \u201cThe Pablo Self-Defining Data Format.\u201d Department of Computer Science, University of Illinois, ftp:\/\/ bugle.cs.uiuc.edu\/pub\/Release\/Documentation\/SDDF.ps, April."},{"key":"bibr3-109434200201600401","doi-asserted-by":"publisher","DOI":"10.1155\/1999\/304639"},{"key":"bibr4-109434200201600401","unstructured":"Benkner S., Andel S., Blasko R., Brezany P., Celic A., Chapman B., Egg M., Fahringer T., Hulman J., Hou Y., Kelc E., Mehofer E., Moritsch H., Paul M., Sanjari K., Sipkova V., Velkov B., Wender B., and Zima H. 1995. 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