Photon mapping places an enormous burden on the memory hierarchy. Rendering a 512×512 image of a simple scene can require more than 196GB of raw bandwidth to the photon map data structure. This bandwidth is a major obstacle to real time photon mapping. This paper investigates two approaches for reducing the required bandwidth: 1) reordering the kNN searches; and 2) cache conscious data structures. Using a Hilbert curve reordering, we demonstrate an experimental lower bound of 15MB of bandwidth for the same scene. Unfortunately, this improvement of four orders of magnitude requires a prohibitive amount of intermediate storage. We introduce two novel cost-effective algorithms that reduce the bandwidth by one order of magnitude. Scenes of different complexities are shown to exhibit similar reductions in bandwidth. We explain why the choice of data structure does not achieve similar reductions. We also examine the interaction of query reordering with two photon map acceleration techniques, importance sampling and the irradiance cache. Query reordering exploits the additional coherence that arises from the use of importance sampling in scenes with glossy surfaces. Irradiance caching also benefits from query reordering, even when complex surface geometry reduces the effectiveness of the irradiance cache.