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 court cases of the 12 Asia-Pacific computers structure convention (ACSAC 2007), which was once hosted in Seoul, Korea on August 23-25, 2007. This convention has frequently been a discussion board for top researchers within the Asian, American and Oceanian areas to percentage fresh development and the newest ends up in either architectural and procedure concerns. long ago few years the c- ference has turn into extra foreign within the experience that the geographic foundation of p- ticipants has develop into broader to incorporate researchers from everywhere in the international, incl- ing Europe and the center East. This 12 months, we got ninety two paper submissions. each one submission was once reviewed via no less than 3 fundamental reviewers besides as much as 3 secondary reviewers. the complete variety of accomplished stories reached 333, giving every one submission 3.6 studies on standard. the entire stories have been rigorously tested through the paper choice approach, and eventually 26 papers have been authorized, leading to an popularity expense of approximately 28%. the chosen papers surround quite a lot of issues, with a lot emphasis on and software program recommendations for cutting-edge multicore and multithreaded architectures.
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24 programs, shown in Figure 5(a), that need higher sampling rates to achieve a targeted accuracy. Table 2 shows the entropy of the SPEC CPU2000 FP benchmarks. Two programs (swim, art ) show a low entropy (0 ≤ E < 5). Three programs (lucas, applu, fma3d ) show a high entropy (E ≥ 8). Three programs that show high entropy also exactly match the programs, shown in Figure 5(b), that need higher sampling rate. The results strongly suggest that entropy is a good metric to select the sampling rate for SPEC CPU2000 benchmarks (INT, FP).
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. 6. Average difference prediction accuracy with increasing threshold value Local history entries Fig.
3(b), the function branch_profile will record the times when the cond1 is true. This initial proﬁling phase lasts a ﬁxed amount of iterations, such as 200. When proﬁling phase ends, a runtime decision routine like the function decision_routine in Fig. 3(c) is called. It analyzes the collected proﬁling results to decide current frequent violation candidate sets (FVC). According to the mapping correlation between diﬀerent FVC sets and ahead-of-time optimized code versions, it chooses a currently optimal version for the execution of rest iterations.
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.)