The domains of parallel and distributed computing have been converging continuously up to the degree that state-of-the-art server computer systems incorporate characteristics from both domains: They comprise a hierarchy of enclosures, where each enclosure houses multiple processor sockets and each socket again contains multiple memory controllers. A global address space and cache coherency are facilitated using multiple layers of fast interconnection technologies even across enclosures. The growing popularity of such systems creates an urge for efficient mappings of cardinal algorithms onto such hierarchical architectures. However, the growing complexity of such systems and the inconsistencies between implementation strategies of different hardware vendors make it increasingly harder to do find efficient mapping strategies that are universally valid. In this paper, we present scalable optimization and mapping strategies in a case study of the popular Scale-Invariant Feature Transform (SIFT) computer vision algorithm. Our approaches are evaluated using a state-of-the-art hierarchical Non-Uniform Memory Access (NUMA) system with 240 physical cores and 12 terabytes of memory, apportioned across 16 NUMA nodes (sockets). SIFT is particularly interesting since the algorithm utilizes a variety of common data access patterns, thus allowing us to discuss the scaling properties of optimization strategies from the distributed and parallel computing domains and their applicability on emerging server systems.