By Krisztián Flautner (auth.), Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong (eds.)
This booklet constitutes the refereed complaints of the twenty second overseas convention on structure of Computing structures, ARCS 2009, held in Delft, The Netherlands, in March 2009.
The 21 revised complete papers awarded including three keynote papers have been conscientiously reviewed and chosen from fifty seven submissions. This year's distinctive concentration is decided on power information. The papers are geared up in topical sections on compilation applied sciences, reconfigurable and purposes, significant parallel architectures, natural computing, reminiscence architectures, enery information, Java processing, and chip-level multiprocessing.
Read or Download Architecture of Computing Systems – ARCS 2009: 22nd International Conference, Delft, The Netherlands, March 10-13, 2009. Proceedings PDF
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Additional resources for Architecture of Computing Systems – ARCS 2009: 22nd International Conference, Delft, The Netherlands, March 10-13, 2009. Proceedings
Most performance monitor tools provide the functionality to count the Instruction Pointer (IP) addresses encountered during the sampling, revealing a runtime proﬁle of the program . By analyzing the collected counts of IP addresses, performance engineers can ﬁgure out which part in the program is most frequently executed, in a statistical manner. Intuitively, the more the IP address is encountered in the sampling, the likelier the IP address is a hotspot of the program. This work is supported by EPSRC grant - Liquid Circuits: Automated Dynamic Hardware Acceleration of Compute-Intensive Applications.
C Springer-Verlag Berlin Heidelberg 2009 Evaluating Sampling Based Hotspot Detection 29 However, the periodical sampling may not match with the real execution of the program due to its statistical nature. Fig. gcc with one of the input data sets. For legibility, the counts are displayed in their percentages against the total sample number and the total instruction number respectively. From the ﬁgure, we can see that the sampled counts are diﬀerent from the real execution counts of the IP addresses.
The other is that randomization of the sampling period is often used in sampling to avoid biased sampling result. , (IPm , cm )}, where ci indicates the count of IPi in the sampling, and m is the number of diﬀerent IP addresses collected in the sampling. On the other hand, we can use instrumentation tool to get the execution count of all the basic blocks of the program with the speciﬁc input set. , (BBLl , bcl ) be the counts of all basic blocks of the program with the speciﬁc input set, where bci is the execution count of the basic block BBLi , and l is the number of diﬀerent basic blocks.
Architecture of Computing Systems – ARCS 2009: 22nd International Conference, Delft, The Netherlands, March 10-13, 2009. Proceedings by Krisztián Flautner (auth.), Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong (eds.)