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I spent the first minute or two thinking about graph traversal algorithms before realizing none of that mattered.
Clarify the graph representation and whether the GPUs are given as a list of nodes or need to be extracted from the graph. Then, iterate through all GPUs, using a hash map to group them by node ID, and return the mapping. Discuss time and space complexity, and consider edge cases like empty input or duplicate node IDs.
Pro tip: Mention that this is essentially a group-by operation and can be done in a single pass with O(N) time, but also discuss how it might scale in a distributed system if the graph is huge, showing awareness of system design.
Ask whether the graph is given as an adjacency list or matrix, and whether we need to traverse the graph or if we already have a list of GPU nodes. Confirm the output format: a mapping from node ID to list of GPUs.
Use a hash map (dictionary) to map node IDs to lists of GPUs. This allows O(1) average-time insertion and lookup. If the graph is large, consider memory implications.
Traverse all GPU nodes (either from the graph or a provided list). For each GPU, extract its node ID and append the GPU to the corresponding list in the hash map, creating a new list if the node ID is not yet present.
Consider empty input, GPUs with missing node IDs, or duplicate GPUs. Decide whether to include empty lists for node IDs with no GPUs (usually not).
State that the time complexity is O(N) where N is the number of GPUs, and space complexity is O(N) for the output. If the graph is very large, discuss distributed grouping using MapReduce or partitioning by node ID.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.