By Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong
This ebook 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 provided including three keynote papers have been conscientiously reviewed and chosen from fifty seven submissions. This year's distinctive concentration is decided on power expertise. The papers are equipped in topical sections on compilation applied sciences, reconfigurable and functions, monstrous parallel architectures, natural computing, reminiscence architectures, enery information, Java processing, and chip-level multiprocessing.
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Our experimental results show that the FPGA implementation achieves an order of magnitude speedup compared to the NCBI BLASTN software running on a general purpose computer. Keywords: genomic sequence analysis, Bloom filter, bioinformatics, reconfigurable computing. 1 Introduction Scanning genomic sequence databases is a common and often repeated task in molecular biology. 5 to 2. The scan operation consists of finding similarities between a particular query sequence and all sequences of a bank.
The superlinear speedup in case of the processor array implementations is because of the increasing clock frequency for larger numbers of PEs. The moderate cost and power increase is caused by the decreasing amount of intermediate data which have to be stored internally in the processor array. Since the difference for the u = 64 implementations was tremendous, several other fully unrolled/full size versions have been studied. The results are shown in Fig. 8. In case when enabling the DSP48 slices, the throughput for the unrolled approach sharply falls for higher numbers of taps.
Section 6 concludes the paper. 2 Bloom Filter Data Structure A Bloom filter is a space-efficient probabilistic data structure. It represents a set of given keys in a bit-vector. Insertion and querying of keys are supported using several independent hash functions. Bloom filters gain their space-efficiency by allowing a false positive answer to membership queries. Space savings often outweigh this drawback in applications where a small false positive rate can be tolerated, particularly when space resources are at a premium.