By Pen-Chung Yew (auth.), Lynn Choi, Yunheung Paek, Sangyeun Cho (eds.)
On behalf of this system and organizing committee participants of this convention, we th are happy to provide you with the complaints 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 development and the most recent leads to either architectural and process concerns. some time past few years the c- ference has turn into extra overseas 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 got ninety two paper submissions. every one submission used to be reviewed by means of no less than 3 fundamental reviewers in addition to as much as 3 secondary reviewers. the complete variety of accomplished experiences reached 333, giving every one submission 3.6 studies on usual. all of the studies have been rigorously tested through the paper choice method, and at last 26 papers have been accredited, leading to an reputation expense of approximately 28%. the chosen papers surround quite a lot of subject matters, with a lot emphasis on and software program thoughts 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
However, the resulting performance loss can be alleviated by conservatively selecting loop candidates. 4 Conclusion From the above results it can be concluded that the two-phase online proﬁling approach has the comparable ability to the oﬄine proﬁling when it is taken as a guide to transaction partition optimization and identifying the loops suitable to An Online Proﬁle Guided Optimization Approach 39 be speculatively parallelized. This approach can guide the speculative optimization of the applications lacking appropriate training inputs, while the traditional oﬄine method doesn’t work for them.
The function field of each entry in the BDHT holds n saturated confidence counters, in the range -4 to +4, which are associated with the n Markov predictors. For a pattern length k, where 1 ≤ k ≤ n, the Markov predictors will furnish a value prediction if that repeating pattern is stored at least once in its h history values. In the case of a correctly predicted branch, its confidence saturating counter is incremented and decremented in the case of a misprediction. The Markov prediction is then replicated to match its confidence counter, so long as that confidence counter is >0.
To support eﬃcient pre-JIT type of compilation, this paper proposes and studies an entropy-based proﬁle characterization and classiﬁcation method. This paper ﬁrst shows that highly accurate proﬁles can be obtained by merging a number of proﬁles collected over repeated executions with relatively low sampling frequency for the SPEC CPU2000 benchmarks. It also shows that simple characterization of the proﬁle with information entropy can be used to guide sampling frequency of the proﬁler in an autonomous fashion.