Well, you can walk spatial partitions with trees in ways that become clunky with hashes (taking a branch means all children are 'near' each other). Also, B-trees might work better in cached architectures -- this is certainly true with respect to disk accesses but I'm not sure if that carries over to L3/L2/L1 cache.
B-Trees actually have superior cache performance compared to other types of trees (such as T-Trees or BST Trees). In part, this is because B-Trees have a higher density of actual data to pointers.
There is a tradeoff of computation (doing binary search inside the node) and amount of storage (keeping less pointers). In environments where memory access is much more expensive than a CPU instruction, it is preferable to perform these computations than to have to read all the extra pointer data to jump to the right places.
In fact, a breed of cache-friendly data structures are precisely based on the B+Trees but with even less pointers, having the algorithm compute these pointers instead (CSS-, CSB-Trees)
It does carry over to L3/L2/L1 cache, but only if the tree is designed to benefit from that. See http://blogs.msdn.com/b/devdev/archive/2007/06/12/cache-obli... for one strategy to do that. (With that structure lookups become faster, but walking the tree in order becomes difficult.)
This is a really great article and I wish I could give you some more points to make this appear higher. I once used a cache-oblivious matrix transpose algorithm that was startlingly simple and effective but I didn't know the approach had been so widely applied. Thanks!
