Point-to-point latency is one of the most important metrics for high performance computer networks and is used widely in communication performance modeling, link-failure detection, and application optimization. However, it is often hard to determine the full-scale point-to-point latency of large scale HPC networks since it often requires measurements to the square of the number of terminal nodes. In this paper, we propose an efficient method to generate measurement plans for arbitrary indirect HPC networks and reduces the measurement requirements from $$O(n^2)$$ to m, which is often O(n) in modern indirect networks containing n nodes and m links, thus significantly reduces the latency measure overhead. Both analysis and experiments show that the proposed method can reduce the overhead of large-scale fat-tree networks by orders of magnitudes.