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Team:Johns Hopkins-Software/Cloud
From 2012.igem.org
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| - | <font size="2" color = "white">Autogene harnesses the power of the cloud to perform computationally intense tasks at record speeds. Cloud computing is known as the use of software and hardware services across a network, often the internet. The advantages of using the cloud is that an organization would not have to maintain their own hardware, so they can save on the cost of the technology while ensuring the quality of performances. They can increase access as essentially anyone with the authorized credentials could access the data or software through the internet, and are not limited to any physical location. And of course, the cloud can handle many demanding tasks. Using multiple machines to process work in parallel, performance could be sped up to a small fraction of the time. | + | <font size="2" color = "white"><br>Autogene harnesses the power of the cloud to perform computationally intense tasks at record speeds. Cloud computing is known as the use of software and hardware services across a network, often the internet. The advantages of using the cloud is that an organization would not have to maintain their own hardware, so they can save on the cost of the technology while ensuring the quality of performances. They can increase access as essentially anyone with the authorized credentials could access the data or software through the internet, and are not limited to any physical location. And of course, the cloud can handle many demanding tasks. Using multiple machines to process work in parallel, performance could be sped up to a small fraction of the time. |
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In the case of the Autogene alignment algorithm, we wrote a client script that communicates with the cloud backend, running two tiers of algorithms that splits up the job into many subjobs running in parallel. We have tested this on an alignment of the PUC18 gene, which consists of a sequence of 2,680 letters, against a library of 17,498 yeast features, each about 400 base-pairs long. Running conventionally without the cloud, we found that it would take about 39 minutes to complete this alignment. Running it on the cloud with 10 processors we cut the time to three minutes, and running it with 30 processors we cut it to nearly one minute. PUC18 is a relatively unintimidating-sized sequence. Considering how many sequences of interest can be up to thousands of letters in length, and how libraries can have countless features, which could cause alignments to take weeks to complete, certain alignment tasks would require more memory than a local machine would be able to handle, so this is the kind of job that could only be done through a cloud server. With this kind of improvement, we are making the impossible in biology possible.</td> | In the case of the Autogene alignment algorithm, we wrote a client script that communicates with the cloud backend, running two tiers of algorithms that splits up the job into many subjobs running in parallel. We have tested this on an alignment of the PUC18 gene, which consists of a sequence of 2,680 letters, against a library of 17,498 yeast features, each about 400 base-pairs long. Running conventionally without the cloud, we found that it would take about 39 minutes to complete this alignment. Running it on the cloud with 10 processors we cut the time to three minutes, and running it with 30 processors we cut it to nearly one minute. PUC18 is a relatively unintimidating-sized sequence. Considering how many sequences of interest can be up to thousands of letters in length, and how libraries can have countless features, which could cause alignments to take weeks to complete, certain alignment tasks would require more memory than a local machine would be able to handle, so this is the kind of job that could only be done through a cloud server. With this kind of improvement, we are making the impossible in biology possible.</td> | ||
Revision as of 04:34, 1 October 2012
