The use of GPUs for scientific computation has undergone phenomenal growth over the past decade, as hardware originally designed with limited instruction sets for image generation and processing has become fully programmable and massively parallel. This talk discusses the classes of problem that can be attacked with such tools, as well as some practical aspects of implementation. A direction for future research by the speaker is also discussed.