Windows Azure is Microsoft's cloud-based server service, but Microsoft wants to show the world that it can do more than just store data for major websites. In a recent post on the Next at Microsoft blog, the company also shows how companies are using Windows Azure to crunch some big numbers in the name of medical research.
The blog post stated that this kind of work require researchers to look at a ton of data from a lot of individuals. There is a computer algorithm that can be run by PCs to help find and cut down on the number of false positives that occur in this type of work. The technique is called linear mixed models (LMMs). However, the blog points out that this kind of work can take a lot of time, as well as a lot of processing power.
Microsoft has now developed a modification of LLMs called the Factored Spectrally Transformed Linear Mixed Model (FaST-LMM). The blog states, "It allows much larger datasets to be processed and can, therefore, detect more subtle signals in the data."
Microsoft Research decided to use the FaST-LLM algorithm to check on 63,524,915,020 pairs of genetic markers that were sent to the division by the Wellcome Trust. Windows Azure handled the actual computations. The blog stated, "27,000 CPU’s were used over a period of 72 hours. 1 million tasks were consumed —the equivalent of approximately 1.9 million compute hours. If the same computation had been run on an 8-core system, it would have taken 25 years to complete."
The end result is that Windows Azure and the FaST-LLM algorithm were combined to not only go through a ton of data, but went through that information in a relatively short amount of time. More information on this kind of work can be seen in the video above.
Source: Next at Microsoft
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