Model the grid as a 2D array and simulate each tick using a BFS-like propagation, tracking infection timestamps and immunity expiration. For the variant, maintain a separate queue or timestamp array for when immunity wears off, and process events in chronological order. Discuss trade-offs between time-step simulation and event-driven approaches.
Pro tip: Clarify upfront whether the spread is simultaneous (all infected cells spread at once) or sequential, as this drastically changes the implementation and is a common pitfall. Also, mention that using a queue for newly infected cells can optimize the simulation to O(N*M) per tick in the worst case.
Ask about grid size limits, whether spread is simultaneous, and if immunity is permanent or temporary. Confirm input/output format and edge cases like no infected cells initially.
Use a 2D array for cell states (e.g., 0=healthy, 1=infected, 2=immune) and optionally a queue for infected cells to process. For immunity duration, maintain a parallel array of expiration times or a priority queue of events.
Iterate through ticks: for each infected cell, check its neighbors (4 or 8 directions) and infect healthy ones. Use a temporary list to avoid cascading within the same tick if simultaneous spread is required.
Track when each immune cell becomes susceptible again (e.g., store infection time + D). At each tick, first update immunity statuses, then perform infection spread. Use a queue or sorted events to efficiently handle expirations.
Discuss time complexity: O(T * N * M) for naive simulation, where T is number of ticks. Optimize by only processing active cells (infected or newly susceptible) using queues, achieving O(N*M + total infections) overall.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I kind of fell apart a little.
Start by clarifying the simulation's characteristics (e.g., grid size, update rules, access patterns) and then propose a distributed architecture that partitions the grid across multiple machines. Focus on data partitioning, communication patterns, and fault tolerance, while discussing trade-offs between different approaches.
Pro tip: Emphasize that the optimal design depends on the simulation's update locality; for example, if updates only affect neighboring cells, use a halo exchange pattern to minimize communication. Also, mention that you would prototype with a small cluster to measure performance and iterate.
Ask about grid size, update frequency, communication patterns, and consistency requirements to understand the problem scope. Identify if the simulation is iterative (e.g., time steps) and if cells interact only with neighbors.
Decide how to split the grid across machines: e.g., 2D block partitioning, tiling, or space-filling curves. Consider load balancing and minimize cross-partition dependencies.
Define how partitions exchange boundary data (e.g., halo exchange) and synchronize time steps. Use efficient communication patterns (e.g., MPI, gRPC) and consider asynchronous updates if acceptable.
Plan for node failures (e.g., checkpointing, replication) and ensure the design scales horizontally. Discuss trade-offs between consistency, latency, and throughput.
Propose metrics (e.g., throughput, latency, resource utilization) and a plan to benchmark and optimize the design. Mention potential bottlenecks and how to address them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.