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Figure 2

Figure 2. From: Searching for SNPs with cloud computing.

Crossbow workflow. Previously copied and pre-processed read files are downloaded to the cluster, decompressed and aligned using many parallel instances of Bowtie. Hadoop then bins and sorts the alignments according to primary and secondary keys. Sorted alignments falling into each reference partition are then submitted to parallel instances of SOAPsnp. The final output is a stream of SNP calls made by SOAPsnp.

Ben Langmead, et al. Genome Biol. 2009;10(11):R134-R134.
2.
Figure 1

Figure 1. From: Searching for SNPs with cloud computing.

Number of worker CPU cores allocated from EC2 versus throughput measured in experiments per hour: that is, the reciprocal of the wall clock time required to conduct a whole-human experiment on the Wang et al. dataset []. The line labeled 'linear speedup' traces hypothetical linear speedup relative to the throughput for 80 CPU cores.

Ben Langmead, et al. Genome Biol. 2009;10(11):R134-R134.
3.
Figure 3

Figure 3. From: Searching for SNPs with cloud computing.

Four basic steps to running the Crossbow computation. Two scenarios are shown: one where Amazon's EC2 and S3 services are used, and one where a local cluster is used. In step 1 (red) short reads are copied to the permanent store. In step 2 (green) the cluster is allocated (may not be necessary for a local cluster) and the scripts driving the computation are uploaded to the master node. In step 3 (blue) the computation is run. The computation download reads from the permanent store, operates on them, and stores the results in the Hadoop distributed filesystem. In step 4 (orange), the results are copied to the client machine and the job completes. SAN (Storage Area Network) and NAS (Network-Attached Storage) are two common ways of sharing filesystems across a local network.

Ben Langmead, et al. Genome Biol. 2009;10(11):R134-R134.

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