Recently, graphics hardware architectures have begun to emphasize versatility, offering rich new ways to programmatically reconfigure the graphics pipeline. In this paper we explore whether current graphics architectures can be applied to problems where general-purpose vector processors might traditionally be used. We develop a programming framework and apply it to a variety of problems, including matrix multiplication and 3-SAT. Comparing the speed of our graphics card implementations to standard CPU implementations, we demonstrate startling performance improvements in many cases, as well as room for improvement in others. We analyze the bottlenecks and propose minor extensions to current graphics architectures which would improve their effectiveness for solving general-purpose problems. Based on our results and current trends in microarchitecture, we believe that efficient use of graphics hardware will become increasingly important to high-performance computing on commodity hardware.