As the cloud computing paradigm has gained prominence, the need for verifiable computation has grown increasingly urgent. Protocols for verifiable computation enable a weak client to outsource difficult computations to a powerful, but untrusted server, in a way that provides the client with a guarantee that the server performed the requested computations correctly. By design, these protocols impose a minimal computational burden on the client, but they require the server to perform a very large amount of extra bookkeeping to enable a client to easily verify the results. Verifiable computation has thus remained a theoretical curiosity, and protocols for it have not been implemented in real cloud computing systems. In this paper, we assess the potential of parallel process- ing to help make practical verification a reality, identifying abundant data parallelism in a state-of-the-art general- purpose protocol for verifiable computation. We implement this protocol on the GPU, obtaining 40-120× server-side speedups relative to a state-of-the-art sequential implementation. For benchmark problems, our implementation thereby reduces the slowdown of the server to within factors of 100-500× relative to the original computations requested by the client. Furthermore, we reduce the al- ready small runtime of the client by 100x. Our results demonstrate the immediate practicality of using GPUs for verifiable computation, and more generally, that protocols for verifiable computation have become sufficiently mature to deploy in real cloud computing systems.