Cloud Computing—What's in It for Me as a Scientist?

Computational tasks that are inherently parallel, from simulations to student assignments, can be run faster on the data center resources of public clouds. Many scientists would love access to large-scale computational resources but find that the programming demands of using a supercomputer—as well as the cost and queuing time—are too daunting. Privately owned cloud computers—large data centers filled with computers that mainly run their company's software—are now becoming available to outside users, including scientists and educators. Companies are leasing their computing resources on demand from a large shared pool to individuals who run their own software on a pay-as-you-go basis. This approach is an example of cost associativity (1): 1000 computers used for 1 hour costs the same as one computer used for 1000 hours. If your problem can be computed in a way that takes advantage of parallel processing, you can now get the answer 1000 times as fast for the same amount of money.