Breadth-First Search (BFS) is an important computational kernel used as a building-block for many other graph algorithms. Different algorithms and implementation approaches aimed to solve the BFS problem have been proposed so far for various computational platforms, with the direction-optimizing algorithm being the fastest and the most computationally efficient for many real-world graph types. However, straightforward implementation of direction-optimizing BFS for vector computers can be extremely challenging and inefficient due to the high irregularity of graph data structure and the algorithm itself. This paper describes the world’s first attempt aimed to create an efficient vector-friendly BFS implementation of the direction-optimizing algorithm for NEC SX-Aurora TSUBASA architecture. SX-Aurora TSUBASA vector processors provide high-performance computational power together with a world-highest bandwidth memory, making it a very interesting platform for solving various graph-processing problems. The implementation proposed in this paper significantly outperforms the existing state-of-the-art implementations both for modern CPUs (Intel Skylake) and NVIDIA V100 GPUs. In addition, the proposed implementation achieves significantly higher energy efficiency compared to other platforms and implementations both in terms of average power consumption and achieved performance per watt.