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Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor. Until recently, performance improvement was not difficult. Processors just kept getting faster. Waiting a year for the customer's hardware to be upgraded was a valid optimization strategy. Nowadays, however, individual processors don't get much faster; systems just get more of them. Much comment has been made on coding paradigms to target multiple-processor cores, but the data-parallel paradigm is a newer approach that may just turn out to be easier to code to, and easier for processor manufacturers to implement. This article provides a high-level description of data-parallel computing and some practical information on how and where to use it. It also covers data-parallel programming environments, paying particular attention to those based on programmable graphics processors.