Merging two sorted arrays is a prominent building block for sorting and other functions. Its efficient parallelization requires balancing the load among compute cores, minimizing the extra work brought about by parallelization, and minimizing inter-thread synchronization requirements. Efficient use of memory is also important. We present a novel, visually intuitive approach to partitioning two input sorted arrays into pairs of contiguous sequences of elements, one from each array, such that 1) each pair comprises any desired total number of elements, and 2) the elements of each pair form a contiguous sequence in the output merged sorted array. While the resulting partition and the computational complexity are similar to those of certain previous algorithms, our approach is different, extremely intuitive, and offers interesting insights. Based on this, we present a synchronization-free, cache-efficient merging (and sorting) algorithm. While we use a shared memory architecture as the basis, our algorithm is easily adaptable to additional architectures. In fact, our approach is even relevant to cache-efficient sequential sorting. The algorithms are presented, along with important cache-related insights.