By Ruby B. Lee (auth.), Thambipillai Srikanthan, Jingling Xue, Chip-Hong Chang (eds.)
On behalf of the ProgramCommittee, we're happy to offer the lawsuits of the 2005 Asia-Paci?c computers structure convention (ACSAC 2005) held within the attractive and dynamic state of Singapore. This convention was once the 10th in its sequence, one of many prime boards for sharing the rising learn ?ndings during this ?eld. In session with the ACSAC steerage Committee, we chosen a - member software Committee. This application Committee represented a large spectrum of analysis services to make sure a very good stability of analysis components, - stitutions and adventure whereas retaining the top of the range of this convention sequence. This year’s committee used to be of a similar dimension as final yr yet had 19 new faces. We acquired a complete of 173 submissions that's 14% greater than final 12 months. each one paper used to be assigned to at the very least 3 and now and again 4 ProgramC- mittee contributors for overview. anyplace beneficial, the committee contributors referred to as upon the services in their colleagues to make sure the top attainable caliber within the reviewing strategy. consequently, we obtained 415 experiences from this system Committee participants and their one zero five co-reviewers whose names are stated inthe proceedings.Theconferencecommitteeadopteda systematicblind evaluate technique to supply a good overview of all submissions. finally, we permitted sixty five papers on a huge variety of subject matters giving an attractiveness expense of 37.5%. we're thankful to the entire software Committee participants and the co-reviewers for his or her e?orts in finishing the stories inside a decent schedule.
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Extra info for Advances in Computer Systems Architecture: 10th Asia-Pacific Conference, ACSAC 2005, Singapore, October 24-26, 2005. Proceedings
Suppose there is a ’OneMove’ diﬀerence in time slots i and j between two instruction schedules X1 and X2 as shown in Table 1. Let A = a1 + a2 + ... + an and B = b1 + b2 + ... + bm . Then the diﬀerence of the objective function values deﬁned by (13) for X1 and X2 is given by m α P V (X2 , ξ)α inf − P V (X1 , ξ)inf = n (2bi c)α inf − i=1 (2ai c)α inf (19) i=1 Proof. The objective function deﬁned by (13) can be computed as a sum of the contributions from time slots i and j and that of the rest of the time slots.
On Design, Automation and Test in Europe (DATE'03), pp. 11064-11069, March 2003. 19. D. V. Veidenbaum and A. Nicolau. “Reducing Data Cache Energy Consumption via Cached Load/Store Queue”. Proc. Int. Symp. on Low Power Electronics and Design (ISLPED'03), pp. 252-257, August 2003. 20. M. D. Powell, A. Agarwal, T. Vijaykumar, B. Falsafi, and K. Roy. “Reducing SetAssociative Cache Energy via Way-Prediction and Selective Direct-Mapping”. Proc. Int. Symp. on Microarchitecture, December 2001. 21. E. Rotenberg, S.
The three main elements of this algorithm are outlined as follows. 1. Initial Population: An initial population of candidate schedules is a set of feasible schedules created randomly and ”seeded” with schedules obtained through conventional (non-power-aware) scheduling algorithms. 2. Fitness Evaluation and Selection: The objective function deﬁned by (13) is used to evaluate the ﬁtness of schedules in each generation. Then they are sorted non-decreasingly in terms of their ﬁtness. In order to compute the objective function (13) of a given schedule X, the following rough simulation process is used.