By Pen-Chung Yew (auth.), Lynn Choi, Yunheung Paek, Sangyeun Cho (eds.)
On behalf of this system and organizing committee contributors of this convention, we th are happy to offer you with the court cases of the 12 Asia-Pacific desktops structure convention (ACSAC 2007), which was once hosted in Seoul, Korea on August 23-25, 2007. This convention has ordinarily been a discussion board for prime researchers within the Asian, American and Oceanian areas to percentage contemporary growth and the most recent ends up in either architectural and method concerns. long ago few years the c- ference has turn into extra foreign within the experience that the geographic foundation of p- ticipants has turn 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 by means of no less than 3 fundamental reviewers besides as much as 3 secondary reviewers. the entire variety of accomplished experiences reached 333, giving every one submission 3.6 studies on regular. all of the studies have been conscientiously tested throughout the paper choice procedure, and eventually 26 papers have been accredited, leading to an popularity expense of approximately 28%. the chosen papers surround a variety of issues, with a lot emphasis on and software program strategies for cutting-edge multicore and multithreaded architectures.
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Additional resources for Advances in Computer Systems Architecture: 12th Asia-Pacific Conference, ACSAC 2007, Seoul, Korea, August 23-25, 2007. Proceedings
9. Difference prediction accuracies by combining multiple partial matches through simple voting and confidence-based voting Unbiased Branches: An Open Problem 25 algorithms, a simple voting algorithm and a confidence voting algorithm. Our results show that the average prediction accuracy of the confidence voting algorithm is marginally better than the simple voting algorithm, as shown in Figure 9. 4 Neural-Based Branch Difference Global and Local Prediction In our second alternative approach we developed a family of neural-based branch difference predictors.
1 Similarity Metric In order to evaluate the similarity between a sampled proﬁle and the “complete proﬁle”, we deﬁne a “similarity comparison metric” between the two proﬁles. Since our proﬁle is a list of frequency counts of distinct PC addresses sampled, it can be represented as an 1-dimensional vector. To compute the linear distance between two vectors, we used the Manhattan distance shown in the following equation as a similarity comparison metric. 0 ai , bi : relative freq. of ith distinct PC addr.
The PC addresses of ai and bi are the same. The ai or bi could be zero when no matching samples are collected. If two proﬁles are identical, S becomes 1. Baseline Proﬁle to Determine Similarity of Sampled Proﬁles. Instead of using instrumented proﬁles, we use a merged proﬁle generated from very high Entropy-Based Proﬁle Characterization and Classiﬁcation 43 frequency sampling rates over multiple runs as the baseline proﬁle. We collected sampled proﬁles three times using each of the six diﬀerent sampling rates (one sample every 634847, 235381, 87811, 32973, 34512, 32890 instructions), and generate a baseline proﬁle by merging the 18 proﬁles for each benchmark program.
Advances in Computer Systems Architecture: 12th Asia-Pacific Conference, ACSAC 2007, Seoul, Korea, August 23-25, 2007. Proceedings by Pen-Chung Yew (auth.), Lynn Choi, Yunheung Paek, Sangyeun Cho (eds.)