Hashlife

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Hashlife is an algorithm for computing the long-term fate of a given starting configuration in various Life rules. The algorithm was invented by Bill Gosper in the early 1980s while he was engaged in research at the Xerox Palo Alto research center. Hashlife was originally implemented on Symbolics Lisp machines with the aid of the Flavors extension.

1 Hashlife
2 Drawbacks
3 See also
4 Reference
5 External links

[edit] Hashlife

Hashlife is designed to exploit large amounts of spatial and time redundancy in most Life rules. For example, in Conway's Life, the maximum density of live cells in a region is only 1/2, and many seemingly random patterns end up as collections of simple still lifes and oscillators.

[edit] Representation

The field is typically treated as a theoretically infinite grid, with the pattern in question centered near the origin. A quadtree is used to represent the field. An update operation takes the square of 2^2k cells, 2^k on a side, at the kth level of the tree, and computes the next generation of the 2^k/2-by-2^k/2 square of cells in the center.

[edit] Hashing

While a quadtree trivially has far more overhead than other simpler representations (such as using a matrix of bits), it allows for various optimizations. As the name suggests, it uses hash tables to store the nodes of the quadtree. Many subpatterns in the tree are usually identical to each other; for example the pattern being studied may contain many copies of the same spaceship, or even large swathes of empty space. These subpatterns will all hash to the same position in the hash table, and thus many copies of the same subpattern can be stored using the same hash table entry. In addition, these subpatterns only need to evaluated once, not once per copy as in other Life algorithms.

This itself leads to significant improvements in resource requirements; for example a generation of the various breeders and spacefillers, which grow at large speeds, can be evaluated in Hashlife using logarithmic space and time.

[edit] Superspeed and caching

A further speedup for many patterns can be further achieved by evolving different nodes at different speeds. For example, one could compute twice the number of generations forward for a node at the (k+1)-th level compared to one at the kth. For sparse or repetitive patterns such as the classical glider gun, this can result in tremendous speedups, paradoxically allowing one to compute bigger patterns at higher generations faster, sometimes exponentially. To take full advantage of this feature, subpatterns from past generations should be saved as well.

Since different patterns are allowed to run at different speeds, some implementations, like Gosper's own hlife program, do not have an interactive display, but simply computes a preset end result for a starting pattern, usually run on the command line. Golly, however, can use Hashlife with a graphical interface, for example.

The typical behavior of a Hashlife program on a conducive pattern is as follows: first the algorithm runs slower compared to other algorithms because of the constant overhead associated with hashing and building the tree; but later, enough data will be gathered and its speed will increase tremendously - the rapid increase in speed is often described as "exploding".

[edit] Drawbacks

Hashlife can consume significantly more memory than other algorithms, especially on moderate-sized patterns with a lot of entropy, or which contain subpatterns poorly aligned to the bounds of the quadtree nodes (ie. power-of-two sizes); the cache is a vulnerable component. It can also consume more time than other algorithms on these patterns. Golly, among other Life simulators, have options for toggling between Hashlife and conventional algorithms.

Hashlife is also significantly more complex to implement. For example, it needs a dedicated garbage collector to remove unused nodes from the cache. While a beginner might be able to write a simple Life player over an afternoon, few Hashlife implementations exist.