For my serialisation package (more on that later; the goals are to maintain sharing/circularity, minimise the number of copies for unboxed data and to support closures), I needed a way to quickly map from objects to some bookkeeping information. At first, that was a hash table. Unfortunately, there is considerable space overhead (SBCL uses chained buckets, so there's the array, the chain, and the key), which can easily destroy runtime on large enough data sets.

Finally, I found a rather hacky way to pin the serialised objects in place, which makes it possible to use raw addresses. For numbers, I stuck to the hash table. For everything else, however, I was looking for a relatively fast and reasonably space-efficient way to map from integers to objects. Cuckoo hash tables spend too much time rehashing on grows. Similarly, sparse vectors as a combination of a bitmask and a dense vector take too much time copying data into larger containers.

To avoid the problem of expensive grows, using some sort of tree structure seemed like a nice solution: each node contains much less data than the complete tree. Unfortunately, trees often have a low branching factor, so a query ends up reading from many locations. As soon as the tree becomes larger than cache, things get pretty ugly. I wanted something with fat nodes, like B-trees. However, finding the right child in a B-tree node isn't particularly cache-friendly either: a binary search is horrible for locality. A correct solution would be to use a cache-aware (ideally cache-oblivious, since I don't like fiddling with magic constants) layout, like van Emde Boas trees, or Bender, Demaine and Farach-Colton's cache-oblivious B-tree.

A lazier solution is to take advantage of the fact that we're dealing with bits, and not just some abstract set of totally-ordered values, and treat our keys (addresses) like strings of bits of constant length (modulo leading zeros). Tries are then natural... But they too tend to have low branching factors. With an arbitrary length of 40 bits (that's 1 TB's worth of addresses, or, rather, since I shift the 4 least significant bits out first, 16 TB), something like using the 20 MSB in the first level, then 10 and 10 in the second and third level seemed natural: an array of 1 million elements isn't that large, as long as there aren't thousands of them, and 1024 elements really isn't that bad either. Moreover, restricting the trie to 3 levels reduces the number of cache misses.

Obviously, it wouldn't be feasible to have a dense array of 1024 or 1M elements for each trie node, when most node will have only a few children, if any. We need another sparse array, but this time with different goals: space efficiency and fast growth aren't such issues anymore, but read and write speeds are.

I borrowed the concept of 'critical bit' from PATRICIA tries, and pluralized it to get a class of rather cheap hash functions that should, on typical workloads, do fine. The idea is to identify bit positions that are identical across all keys, and form an index from the other positions by concatenating them together. In the best case, it takes lg n bits to index from n keys (multiples of a power of 2), and, in the worst case, n bits for 2 keys (no common bit). Fortunately, we'd expect addresses to be closer to the former than the latter.

A lookup then consists of making sure the searched key corresponds to the identical bits (mask and compare), and then concatenating the variable bits together. By looping over each byte and using look-up tables for the popcount and partial concatenating, that operation takes on the order of 30-100 cycle on my 2.16 GHz Core 2.

Insertion, is, as always, a bit hairier. The simple case is when the common bits fit (again, mask and compare). If so, all that is needed is to concatenate the new key to find its index and write in the dense array. Otherwise, a new mask must be computed, and elements from the old dense vector reinserted in the new (at least twice larger) one with the correct indices. Computing a new mask is surprisingly simple: (logandc1 (logxor new-value common-value) common-mask) .

I haven't managed to find a simpler way to do reinsertion than to recurse over the non-common bit positions to generate all the values that fit with the common bit positions (and their values). At least, that way it's possible to compute the old concatenated index on the fly (I haven't found how to do that for the new concatenated index yet, unfortunately). It obviously pays off to add a special case when there is only one key, and store it directly, instead of using a vector of length 1. What was surprisingly also important to performance was to explicitly reuse the vectors that are freed on reinsertions.

By glueing these two pieces together, we get an implementation of sparse vectors that should be able to take advantage of locality in keys (empty or nearly empty spans take very little space), and of (some) patterns in contiguous keys to save space. It's also reasonably performant, since it's expected to incur very few (a half dozen) misses for random access. Interestingly, it also makes predecessor/successor queries efficient, since the concatenating operation preserves ordering (bits are concatenated in order). In comparison with SBCL's hash tables (which aren't the best in the west, but not completely horrible either):

Inserting all the integers in [0, 2^20) takes 0.12s (VS 0.83s), conses 8.5 MB (VS 167 MB);

Inserting 2^20 random integers in [0, 2^20) takes 0.18s (VS 0.81) conses 8.5 MB (vs 83 MB)

Inserting all the multiples of 5 in [0, 5*2^20) takes 0.40s (VS 0.9s) conses 42 MB (VS 167 MB);

Inserting all the multiples of 16 in [0, 16*2^20) takes 0.14s (VS 0.82 s) conses 9.6 MB (VS 167 MB);

Inserting 2^20 random multiples of 256 in [0, 256*2^20) takes 0.72s (VS 1.1s) conses 25 MB (VS 83 MB);

Inserting 2^20 random multiples of 256 in [0, 256*2^22) takes 0.86s (VS 0.85s) conses 49 MB (VS 83 MB);

Inserting 2^20 random integers in [0, 2^30) takes 11s, 8.7 of which in GC, (VS 0.69s) conses 364 MB (VS 83 MB).



For truly random keys, a hash table is better. For my needs, however, the sparse trie is preferable: addresses usually won't be scattered all over the heap, so I can expect less space usage, less consing and faster execution times. Moreover, objects close together will often be traversed together, and the sparse trie can exploit that locality, unlike hash tables. I only compared insertions because it seems to be much slower (often by a factor of 2 or more) than look-ups.