The breadth-first search (BFS) is one of the most important kernels in graph theory. The Graph500 benchmark measures the performance of any supercomputer performing a BFS in terms of traversed edges per second (TEPS). Previous studies have proposed hybrid approaches that combine a well-known top-down algorithm and an efficient bottom-up algorithm for large frontiers. This reduces some unnecessary searching of outgoing edges in the BFS traversal of a small-world graph, such as a Kronecker graph. In this paper, we describe a highly efficient BFS using column-wise partitioning of the adjacency list while carefully considering the non-uniform memory access (NUMA) architecture. We explicitly manage the way in which each working thread accesses a partial adjacency list in local memory during BFS traversal. Our implementation has achieved a processing rate of 11.15 billion edges per second on a 4-way Intel Xeon E5-4640 system for a scale-26 problem of a Kronecker graph with 2 26 vertices and 230 edges. Not all of the speedup techniques in this paper are limited to the NUMA architecture system. With our winning Green Graph500 submission of June 2013, we achieved 64.12 GTEPS per kilowatt hour on an ASUS Pad TF700T with an NVIDIA Tegra 3 mobile processor.