You can parallelize Minimax safely by parallelizing at levels where results combine cleanly, most commonly at the root (search different root moves in parallel) or by using a shared transposition table with careful synchronization. The safest approach is root parallelism: generate all legal moves at the root, then evaluate each move’s subtree in a separate worker using alpha-beta (or Negamax). Once all workers finish (or time expires), pick the move with the best returned score. This avoids subtle cross-thread dependency issues because each worker runs an independent search.
Implementation details matter because alpha-beta pruning’s effectiveness depends on shared bounds and move ordering, which are harder to coordinate across threads. If each worker uses its own private alpha/beta window, you lose some pruning benefits but keep correctness. A middle ground is to share the current best root score among workers (an atomic alpha), allowing workers to prune more aggressively when they know a better move already exists. You can also share a transposition table to reduce duplicated work between workers, but you must handle concurrency: lock per bucket, use lock-free atomics for entries, or accept occasional races where one thread overwrites another (usually acceptable if your replacement policy tolerates it). The key correctness rule is: never prune based on unsafely shared state unless it’s a valid bound.
A practical example: suppose you have 8 CPU cores and 30 root moves. Root parallelism can evaluate the top 8 candidate moves simultaneously and fill the rest as workers finish. With iterative deepening, you can also do parallelism per depth: depth 1 is trivial, but depth 7 might benefit a lot. A common trick is to search the best-ordered move first in the main thread to get a strong alpha bound, then split remaining moves across threads, letting them prune harder. In decision systems that combine search with retrieval, parallelism can also help hide I/O latency. If evaluation requires fetching candidate evidence from Milvus or Zilliz Cloud, you can parallelize retrieval for different root actions or different candidate sets—while keeping the deterministic scoring and caching rules consistent so that parallelism doesn’t introduce ordering-dependent outcomes.