Part 7 – Transposition Table

When you explore the game tree, you end up analysing several times the same positions that can be reached from different combination of moves. You can save time by caching the outcome of previous computation to avoid to re-compute the score of a position you already explored in the past.

The idea is similar to dynamic programming: you trade computational time against memory space. Unfortunately you won’t be able to store all the positions and you have to come up with some strategies to optimize which positions to keep in memory.

Transposition Table

Efficient transposition tables will try to keep the positions that will help you saving the most computation time. It can be either frequent positions or positions that take a lot of time to compute. We implement a strategy favoring storage of the most recently explored nodes.

Our very simple transposition table is mainly a hash table with fixed storage size that overriding previous entries (no collision management). Keeping the most recent positions is a good and simple strategy because recently explored nodes are often close to the current position. This strategy increases the probability to have a positive hit in your cache.

Alpha-beta outcome can be a lower or an upper bound of the actual score. In this version, we will only cache the upper bound results. Storing both upper and lower bound complexifies the implementation and does not bring in practice much improvement. We will consider this smaller optimization later.


Our simple Transposition Table implementation supports 56-bit keys and 8-bit values, making 64-bit entries. Here is the simplified interface we need:

class TranspositionTable {

   * Store a value for a given key
   * @param key: 56-bit key
   * @param value: non-null 8-bit value. null (0) value are used to encode missing data.
  void put(uint64_t key, uint8_t val);

   * Get the value of a key
   * @param key
   * @return 8-bit value associated with the key if present, 0 otherwise.
  uint8_t get(uint64_t key) const;

Check the full detailled implementation of this simple interface.

Implementation in the negamax function is straitforward. We simply check at the beginning of the negaxmax function if the current position is already in the transposition table. If the value is present in the cache it may allow to narrow the exploration window and in some cases, generate an immediate pruning of the exploration. At the end of the negamax function, when we have an upper bound, we simply store the value in the Transposition table.

Full source code corresponding to this part.


For this benchmark, we took a 64MB transposition table (same size as Fhourstone reference). It reduces significantly the number of explored node, especially when a deeper search is needed. We are now able to solve most of the beginning of game test cases in a reasonbale time:

SolverTest Set namemean timemean nb posK pos/s
Transposition Table (strong solver)End-Easy6.531 μs92.8414,220
Transposition Table (strong solver)Middle-Easy5.594 ms207,90037,170
Transposition Table (strong solver)Middle-Medium52.45 ms1,731,00033,000
Transposition Table (strong solver)Begin-Easy4.727 s156,400,00033,090
Transposition Table (strong solver)Begin-Medium8.200 s306,100,00037,330
Transposition Table (strong solver)Begin-HardN/AN/AN/A
Transposition Table (weak solver)End-Easy5.155 μs68.6913,320
Transposition Table (weak solver)Middle-Easy1.072 ms28,75026,830
Transposition Table (weak solver)Middle-Medium23.79 ms752,30031,620
Transposition Table (weak solver)Begin-Easy1.610 s52,840,00032,830
Transposition Table (weak solver)Begin-Medium1.762 s63,930,00036,280
Transposition Table (weak solver)Begin-HardN/AN/AN/A

Tutorial plan