Future exascale supercomputers will be composed of thousands of nodes. In those massive systems, the search for physically close nodes will become essential to deliver an optimal environment to execute parallel applications. Schedulers manage those resources, shared by many users and jobs, searching for partitions in which jobs will run. Significant effort has been devoted to develop allocation strategies that maximize system utilization, while providing partitions that are adequate for the communication demands of applications. In this paper we evaluate a class of strategies based on space-filling curves (SFCs) that search for partitions in which nodes are physically close, compared to other alternatives that relax this requirement (e.g. non-contiguous), or make it even more strict (e.g. contiguous). Several metrics are used to assess the quality of an allocation strategy, some based on system utilization, some others measuring the quality of the resulting partitions. Contiguous allocators suffer from severe degradation in terms of system utilization, while non-contiguous allocators provide inadequate partitions. Somewhere in the middle, SFC allocators offer good system utilization while using quite compact partitions. The final metric to decide which allocator is the best depend on the severity of the slowdown suffered by applications when running in non-optimal partitions.