Architectures for supercomputing have evolved rapidly over the past ten years on essentially two but possibly converging tracks. Firstly those of the Symmetric Multiprocesing cluster type architecture (i.e. the IBM p-series and the Cray XMT-series) and secondly the fully distributed node architecture exemplified by the IBM Blue Gene series. Both have advantages and disadvantages. Problems in the Science and Engineering world have expanded in a fashion exemplified by Parkinson's Law (“work fills the time available”). Science problems, especially those in the physiological domain are themselves defined over vast ranges of scale lengths as will be shown in the following sections. In order to solve problems whose scale lengths vary substantially there are two possible solutions. Either discretise down to the smallest scale with the possibility of producing such large data sets and numbers of equations that the memory requirements become too large for a specific machine or divide the problem into a subset of appropriate length scales and map these discretised sub-domains onto appropriate machine architectures with a fast communication link. The definitions of “appropriate” and “fast” here is determined at present on a case-by-case basis. A generic solution to where the “optimum” boundary should be between differing architectures is a substantial problem in itself. The two architectures each have their own advantages and disadvantages and in the light of this our group at Canterbury have deliberately utilised this to link both compute architectures together to solve a single problem involving the simulation of flow in the cerebro-vasculature. We show that certain mappings of large vascular trees have constraints placed upon them when more than 256 Blue Gene/L processors are used.