15-712 Project Proposal
نویسندگان
چکیده
In virtually all scientific disciplines, technological advances over the past years and decades have resulted in an exponential growth in the quantity of raw scientific data available for analysis. Massive datasets, ranging from hundreds of MBs to many TBs, are increasingly common and available to the public. Spanning the breadth of scientific fields, this publicly accessible information includes NASA astrophysics and astronomy collections [1], partial three-dimensional mappings of the universe [2], earthquake and seismic data [3], complete genomes for a multitude of organisms [4], and so on. However, collecting and publishing raw data is only the beginning of scientific understanding. Unfortunately, the enormous benefits to be gained by thoroughly analyzing these datasets is typically matched by the complexity of such analysis. For instance, sequence alignment is a powerful technique for predicting the function of newly discovered genes [5], but computational limitations prevent the best algorithms available from being used on full datasets. Supercomputers and customized high-performance solutions such as the CLC Bioinformatics Cube [6] can sometimes employed, but the cost of such solutions is prohibitive. Furthermore, for some classes of scientific problems involving huge datasets (including those mentioned above), processing power can be largely wasted even on a supercomputer if the computation is disk-bound. With the advent of multi-core personal computers, clusters of commodity machines have the potential to become a reasonable alternative platform for intense scientific computing. Using clusters of readily available machines in place of supercomputers or problem-specific high-performance machines could not only reduce costs, power consumption, and possibly execution time, but it would also open the multitude of rich, massive datasets to a wider community of researchers, conceivably accelerating the rate of scientific discovery.
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تاریخ انتشار 2007