GPU accelerators have successfully established themselves in modern HPC clusters due to their high performance and energy efficiency. To increase the GPU computational power in a cluster node and tackle larger problems, multi-GPU nodes have become the platform of choice for scientific applications. In a multi-GPU node, GPU devices are interconnected together via different communication channels. Thus, intranode inter-process communications among GPUs may traverse different paths with different latency and bandwidth capacity. As the number of GPUs within a multi-GPU node increases, the topology of GPU interconnects becomes more hierarchical, effectively increasing the heterogeneity of the GPU communication channels. In this paper, we provide evidence that the performance of different intranode GPU communication channels can be considerably different from each other. This is specially true for larger message sizes. Taking this into account, our goal in this work is to efficiently assign the available GPU devices on a multi-GPU node to MPI processes in order to improve the GPU-to-GPU communication performance. We tackle this challenge by proposing a topology-aware GPU selection scheme. Our scheme is capable of efficiently mapping MPI processes to the available intranode GPU devices, in a way that more intensive inter-process GPU communications take place on the more efficient communication channels. Our experimental results show that our topology-aware GPU selection scheme can improve the communication performance of the microbenchmarks with different communication patterns, specifically those with weighted and asymmetrical communications.