By Love Ekenberg, Paul Johannesson (auth.), Subhash Bhalla (eds.)
This ebook constitutes the refereed court cases of the sixth foreign convention on details platforms and administration of knowledge, CISMOD '95, held in Bombay, India, in November 1995.
The booklet provides 14 revised complete papers chosen from a few 60 submissions including six invited papers via major specialists. those papers deal with the critical problems with learn and complicated program within the region of information administration and knowledge structures engineering; they're prepared in sections on question processing, allotted structures, functionality review, wisdom discovery, real-time database structures, product and approach layout, and conceptual modelling.
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Extra resources for Information Systems and Data Management: 6th International Conference, CISMOD '95 Bombay, India, November 15–17, 1995 Proceedings
Eigemann. High-level adaptive program optimization with adapt. In Proceedings of the Symposium on Principles and practices of parallel programming, 2001. 37. M. Voss and R. Eigenmann. Adapt: Automated de-coupled adaptive program transformation. In Proc. ICPP, 2000. 38. R. C. Whaley and J. J. Dongarra. Automatically tuned linear algebra software. In Proc. Alliance, 1998. be Abstract. Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months.
33. M. Stephenson, M. Martin, and U. O’Reilly. Meta optimization: Improving compiler heuristics with machine learning. In Proc. PLDI, pages 77–90, 2003. 34. S. Triantafyllis, M. Vachharajani, and D. I. August. Compiler optimization-space exploration. In Journal of Instruction-level Parallelism, 2005. 35. X. Vera, J. Abella, A. Gonz´alez, and J. Llosa. Optimizing program locality through CMEs and GAs. In Proc. PACT, pages 68–78, 2003. 36. M. Voss and R. Eigemann. High-level adaptive program optimization with adapt.
Fursin et al. Table 1. , for the same baseline performance, avoiding to reset the search each time the phase changes. The length parameter indicates when the phase will change. Thus, the transformation space is searched independently for all phases of a code section. , converging towards different optimizations for different phases of the same code section. Table 1 shows how the phase prediction scheme performs. 4% in most of the cases, except for galgel which exhibits miss rates of 14% and 17% for two time-consuming subroutines.