We started this chapter by learning about device synchronization and the importance of synchronization of operations on the GPU from the host; this allows dependent operations to allow antecedent operations to finish before proceeding. This concept has been hidden from us, as PyCUDA has been handling synchronization for us automatically up to this point. We then learned about CUDA streams, which allow for independent sequences of operations to execute on the GPU simultaneously without synchronizing across the entire GPU, which can give us a big performance boost; we then learned about CUDA events, which allow us to time individual CUDA kernels within a given stream, and to determine if a particular operation in a stream has occurred. Next, we learned about contexts, which are analogous to processes in a host operating system. We learned how to synchronize across an entire CUDA context explicitly and then saw how to create and destroy contexts. Finally, we saw how we can generate multiple contexts on the GPU, to allow for GPU usage among multiple threads or processes on the host.