This paper surveys and places into perspective a number of results concerning the D-BSP (Decomposable Bulk Synchronous Parallel) model of computation, a variant of the popular BSP model proposed by Valiant in the early nineties. D-BSP captures part of the proximity structure of the computing platform, modeling it by suitable decompositions into clusters, each characterized by its own bandwidth and latency parameters. Quantitative evidence is provided that, when modeling realistic parallel architectures, D-BSP achieves higher effectiveness and portability than BSP, without significantly affecting the ease of use. It is also shown that D-BSP avoids some of the shortcomings of BSP which motivated the definition of other variants of the model. Finally, the paper discusses how the aspects of network proximity incorporated in the model allow for a better management of network congestion and bank contention, when supporting a emory abstraction in a distributed-memory environment.