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Biomedical Case Studies in Data Intensive Computing

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Figure

Table 1. Hardware and software configurations of the clusters used in this paper. In addition a traditional 8-node Linux Cluster “Gridfarm” was used to run statistics package R in section 4
Fig. 1. A Data intensive computing architecture
Fig. 4. Performance of Alu Gene Alignments for different parallel patterns
Fig. 5. Comparison of Dryad MapReduce framework with MPI on Smith Waterman Gotoh distance calculations on first step of Alu sequence study pipeline as a function of number of sequences from 10,000 to 50,000
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