Interconnection network represents the backbone of large-scale supercomputers. While the computation has become more energy proportional, the interconnection network has started to become one of the important consumers in the system. Still, interconnection network has received little attention in terms of efficient power consumption. Several hardware-based schemes have been proposed that function either by turning off the links or lowering the voltage/frequency in links when idle or with low

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... ge. Although effective in certain cases, these schemes share the common drawback that they do not have enough global information about the application to manage network power efficiently. This paper, instead, proposes a software prediction support for shutting down inactive lanes of an Infiniband (IB) link. Using n-gram extraction technique as a core of the prediction algorithm we are able to predict with high accuracy when links will be unused. The prediction algorithm detects the repeating patterns of MPI calls during the program execution, on which basis the prediction of the following MPI calls can be realized with relatively high precision. This allow us to know on time when links will be active/idle and invoke the explicit turn-off/on lanes calls. Our power saving mechanism is adopted to be executed within MPI allowing a large number of existing MPI programs to be run without any source code modification. Results show an average power savings in IB switches up to around 33% over the representative HPC applications. On the other hand, HPC applications average suffer only up to 1% increase in execution time.