Start by defining a clear cost model that captures core capabilities and operation types, then formulate the assignment as an optimization problem. Propose a practical heuristic like list scheduling or greedy assignment, and discuss trade-offs between optimality and complexity, including load balancing and communication overhead.
Pro tip: Emphasize that in real systems, the cost model must be calibrated with benchmarks, and that a simple heuristic with good constants often beats a complex optimal algorithm due to overhead and unpredictability.
Characterize each core's capabilities (division-only vs. multiplication+division acceleration) and assign execution costs per operation type. Include factors like throughput, latency, and any data transfer costs.
Model the problem as minimizing total execution time (makespan) by assigning each operation to a core, considering dependencies and core constraints. This is a variant of scheduling on unrelated machines.
Select a practical algorithm such as list scheduling, greedy assignment based on cost ratios, or linear programming relaxation. Justify why it balances near-optimality with computational feasibility.
Discuss trade-offs: optimality vs. runtime of the algorithm, static vs. dynamic assignment, load balancing vs. specialization, and handling of communication or synchronization overhead.
Propose benchmarking and simulation to validate the cost model and heuristic, and describe how to adapt the algorithm if performance deviates from expectations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.