Read e-book online Architecture of Computing Systems; ARCS 2009 PDF

By Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong

ISBN-10: 3642004539

ISBN-13: 9783642004537

This ebook constitutes the refereed lawsuits 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 provided including three keynote papers have been conscientiously reviewed and chosen from fifty seven submissions. This year's targeted concentration is determined on power expertise. The papers are equipped in topical sections on compilation applied sciences, reconfigurable and purposes, colossal parallel architectures, natural computing, reminiscence architectures, enery information, Java processing, and chip-level multiprocessing.

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We have carried out the tests on three evaluation measurements: normalized root mean square error (NRMSE), sample coverage (SC) and order deviation (OD). An intuitive rule is adopted to evaluate the above measurements: – The longer the sampling period is, the less accurate the sampling result is. This intuitive rule means that the difference between the sampling result and the real execution, in our case, the normalized root mean square error and order deviation, should rise with the increase of the sampling period.

Dynamic programming based alignment algorithms whose complexities are quadratic with respect to the length of the sequences can detect similarities between the query sequence and a subject sequence [8]. One frequently used approach to speed up this prohibitively time consuming operation is to introduce heuristics in the search algorithm. Examples of such heuristic tools include BLAST [1], BLAT [6], and Patternhunter [7]. Among these tools BLAST, the Basic Local Alignment Search Tool, is the most widely used software.

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 [1][2][3][4]. 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.

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Architecture of Computing Systems; ARCS 2009 by Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong


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