Start by quantifying the computational complexity and memory requirements to justify why a single machine is infeasible. Then outline a distributed architecture that partitions the search space, uses backtracking with pruning, and incorporates work stealing for load balancing. Finally, discuss termination detection and compare with stochastic methods like simulated annealing, highlighting trade-offs.
Pro tip: Emphasize that the goal is not just parallelism but efficient search space exploration; mention that pruning and heuristics are as important as distribution. Also, note that stochastic methods can find good solutions faster but may not guarantee optimality, so a hybrid approach might be best.
Estimate the search space size (e.g., 100 slots, 1M words, constraints) and memory/CPU requirements. Show that backtracking on a single machine would take years and exceed memory.
Split the crossword into subproblems (e.g., by assigning words to slots or partitioning the grid). Use a master-worker or peer-to-peer model where workers perform backtracking on assigned subproblems.
Apply constraint propagation, forward checking, and word frequency heuristics to reduce the search space. Prune branches early based on letter constraints and dictionary filtering.
Use a work-stealing scheduler (e.g., random stealing or queue-based) to balance load. Minimize communication overhead by batching updates and using efficient protocols (e.g., gossip).
Use a distributed termination algorithm (e.g., Dijkstra's or credit-based) to detect when all workers are idle and no solution exists. Compare with simulated annealing: stochastic methods are simpler but may not guarantee a solution; backtracking is exact but harder to distribute.
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