Download Advances in Computer Systems Architecture: 12th Asia-Pacific by Pen-Chung Yew (auth.), Lynn Choi, Yunheung Paek, Sangyeun PDF
By Pen-Chung Yew (auth.), Lynn Choi, Yunheung Paek, Sangyeun Cho (eds.)
On behalf of this system and organizing committee individuals of this convention, we th are happy to give you with the complaints of the 12 Asia-Pacific desktops structure convention (ACSAC 2007), which used to be hosted in Seoul, Korea on August 23-25, 2007. This convention has commonly been a discussion board for top researchers within the Asian, American and Oceanian areas to proportion fresh development and the most recent leads to either architectural and approach concerns. long ago few years the c- ference has develop into extra overseas within the feel that the geographic beginning of p- ticipants has develop into broader to incorporate researchers from all over the global, incl- ing Europe and the center East. This yr, we bought ninety two paper submissions. every one submission was once reviewed through a minimum of 3 basic reviewers besides as much as 3 secondary reviewers. the complete variety of accomplished studies reached 333, giving every one submission 3.6 studies on ordinary. the entire stories have been conscientiously tested in the course of the paper choice approach, and eventually 26 papers have been authorized, leading to an popularity fee of approximately 28%. the chosen papers surround quite a lot of subject matters, with a lot emphasis on and software program suggestions for cutting-edge multicore and multithreaded architectures.
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Additional info for Advances in Computer Systems Architecture: 12th Asia-Pacific Conference, ACSAC 2007, Seoul, Korea, August 23-25, 2007. Proceedings
In Figure 5 we answer the third question: Which pattern length would achieve the best prediction accuracy? We used a complete PPM predictor with the operand sign Prediction accuracy 90% 85% 80% All 75% Unbiased 70% 65% 1 2 3 4 5 6 7 8 Pattern length Prediction accuracy Fig. 5. Average difference prediction accuracy with increasing pattern length 95% All 85% Unbiased 75% 65% T=1 T=2 T=3 T=4 T=5 T=6 T=7 T=8 Threshold 100% 95% 90% 85% 80% 75% 70% 65% 60% All(T=1) Unbiased(T=1) All(T=7) d ite 25 6 un lim 19 2 12 8 Unbiased(T=7) 64 Prediction accuracy Fig.
E. it needs ﬁrst to run the program once to collect information, afterwards feed back the results to compiler for optimization. The oﬄine method requires a training input in this trial run. But it is not easy to get the representative training input for a program. If the training input doesn’t represent the actual workload, the program may be optimized incorrectly. For example, many commercial programs have multiple functions to ﬁt various requirements, and there isn’t a general training input for proﬁling.
Gellert et al. 5 Conclusion In this study we have validated our previous findings in [1, 8] that current state-ofthe-art branch predictors correlate either insufficient information or wrong information in the prediction of unbiased branches. This led us to consider alternative approaches: the branch difference predictors using PPM and multiple Markov predictors and neural-based perceptron predictors. Our results show that unbiased branches still limit prediction accuracy even with these alternative approaches.