High-end computing is increasingly I/O bound as computations become more data-intensive, and data transport technologies struggle to keep pace with the demands of large-scale, distributed computations. One approach to avoiding unnecessary I/O is to move the processing to the data, as seen in Google's successful, but relatively specialized, MapReduce system. This paper discusses our investigation towards a general solution for enabling in-situ computation in a peta-scale storage system. We believe our work with flexible, application-specific structured storage is the key to addressing the I/O overhead caused by data, partitioning across storage nodes. In order to manage competing workloads on storage nodes, our research in system performance management is leveraged. Our ultimate goal is a, general framework for in-situ data-intensive processing, indexing, and searching, which we expect to provide orders of magnitude performance increases for data-intensive workloads.