Download Center<\/a> later this year) makes it very easy to manage compute resources and schedule your jobs in Windows Azure. You take the proven cluster management tool from Windows Server, connect it to Windows Azure, and then let it do the work. All you need to get started is a Windows Azure account. A set-up wizard takes care of the preparation, and the job scheduler runs your computations.<\/p>\nWhat\u2019s more, there\u2019s no commitment: you can pay as you go, or you can negotiate a discount if you are going to use a lot of core hours.<\/p>\n
As Bill Hilf, general manager of product management for Windows Azure observes, it\u2019s easy to manage a wide range of sizes and types of workloads on Windows Azure. Like Bill, we, too, are extremely enthusiastic about the possibilities offered by the supercomputing prowess of Windows Azure. Such massive computational power is critical for \u201cbig data\u201d studies that increase our understanding of complex systems.<\/p>\n
The genome-wide association study (GWAS) is a case in point. Microsoft Research conducted a 27,000-core run on Windows Azure to crunch data from this study. With the nodes busy for 72 hours, 1 million tasks were consumed\u2014the 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!<\/p>\n
The GWAS offers a powerful approach to identifying genetic markers that are associated with human diseases. It used data from a Wellcome Trust study of the British population, which examined some 2,000 individuals and a shared set of about 13,000 controls for each of seven major diseases. But as in all genome-wide association studies, this study had to overcome this significant problem: to study the genetics of a particular condition, say heart disease, researchers need a large sample of people who have the disorder, which means that some of these people are likely to be related to one another\u2014even if it\u2019s a distant relationship. This means that certain positive associations between specific genes and heart disease are false positives, the result of two people sharing a common ancestor rather than sharing a common propensity for clogged coronaries. In other words, your sample is not truly random, and you must statistically correct for \u201cconfounding,\u201d which was caused by the relatedness of your subjects.<\/p>\n
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Think about supercomputers of the recent past. Just 15 years ago, supercomputers were rare and exotic machines. Government laboratories in the United States and Japan spent hundreds of millions of dollars on custom computing rigs and specialized facilities to house them, in a bid to tackle the world\u2019s toughest problems.<\/p>\n