The push towards exascale computing and the recent introduction of multi-petascale supercomputers have enabled science applications to run complex simulations. However, the gap between compute and I/O has grown wider, even as applications seek to generate and persist increasing amounts of data. Optimizing I/O is challenging and remains a bottleneck at scale. In this paper, we present initial I/O performance results of running Gyrokinetic Toroidal Code (GTC) on Summit, a 200 Petaflop system at Oak Ridge National Laboratory. To manage the complex data in GTC, we use ADIOS, an I/O and data management middleware that provides a rich set of APIs to manage and interact with scientific data. We discuss optimizations performed to obtain improvements in I/O performance and identify a set of challenges that will drive the design and development of next generation data management libraries.