By Mladen Berekovic, Christian Müller-Schloer, Christian Hochberger, Stephan Wong
This publication constitutes the refereed complaints of the twenty second foreign convention on structure of Computing structures, ARCS 2009, held in Delft, The Netherlands, in March 2009.
The 21 revised complete papers offered including three keynote papers have been conscientiously reviewed and chosen from fifty seven submissions. This year's distinct concentration is decided on strength information. The papers are geared up in topical sections on compilation applied sciences, reconfigurable and purposes, big parallel architectures, natural computing, reminiscence architectures, enery understanding, Java processing, and chip-level multiprocessing.
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Extra resources for Architecture of Computing Systems; ARCS 2009
Filtration assumes that good alignments usually contain short exact matches. Such matches can be quickly computed by using data structures such as hash tables or suffix trees. Identified matches are then used as seeds for further analysis. Unfortunately, these data structures require a significant amount of memory space and therefore usually do not scale well on traditional computer architectures. Since filtration is typically the most time-consuming step (Table 1 shows that filtration is around 85% of the overall runtime of NCBI BLASTN ), this approach often results in prohibitive runtimes for large genome sequencing projects.
5 Experiments and Quantitative Evaluation In this section, we compare the two methods described before with respect to resource usage, performance (clock frequency and throughput), and power. Several experiments with different setups have been performed. Also the difference between the usage of dedicated DSP elements of the target devices or the synthesis purely in LUTs has been studied. 1 for a Xilinx Virtex 4 FPGA (xc4vlx100-12ff1513). For the estimation of the dynamic power, Xilinx XPower was used in combination with the post-place & route simulation models of the designs.
715, pp. 398–416. Springer, Heidelberg (1993) Parallelization Approaches for Hardware Accelerators 27 3. : High Performance Compilers for Parallel Computing. , Reading (1996) 4. com 5. com 6. com 7. : SPARK: A High-Level Synthesis Framework for Applying Parallelizing Compiler Transformations. In: Proceedings of the International Conference on VLSI Design, pp. 461–466 (January 2003) 8. : Hardware Synthesis for Multi-Dimensional Time. In: Proceedings of IEEE 14th International Conference on Application-specific Systems, Architectures, and Processors (ASAP), Los Alamitos, CA, USA, pp.