A data engineer loads a social network dataset into cuGraph using a compressed sparse row (CSR) representation. After running a breadth-first search from a single source node, she notices the traversal completes far faster than the equivalent NetworkX run on CPU. Which characteristic of GPU hardware most directly explains this speedup for BFS on large sparse graphs?
- ABFS can expand the entire frontier of unvisited neighbours in parallel across GPU threads, saturating memory bandwidth with concurrent edge reads Correct
- BGPUs have higher single-core clock speeds than CPUs, so each edge inspection executes faster
- CCSR layout stores edges in sorted order, letting cuGraph skip visited nodes via binary search instead of hash-table lookup
- DcuGraph offloads the BFS queue management to the CPU while the GPU handles only the arithmetic, reducing data-transfer overhead
Why A is correct: Each BFS frontier level exposes a large set of independent neighbour checks; GPU threads process these in parallel and the high aggregate memory bandwidth of GPU HBM handles the irregular sparse-memory access pattern at scale
Why B is wrong: GPUs typically run at lower clock speeds than modern CPUs; their advantage comes from massively parallel execution across thousands of cores, not faster individual clock cycles
Why C is wrong: CSR is a compact adjacency format that enables coalesced reads, but BFS visited-node tracking uses a bitset or boolean array, not binary search; the speedup is from parallelism, not search algorithm substitution
Why D is wrong: cuGraph keeps both the graph data and the traversal state resident on the GPU throughout; round-tripping queue state to the CPU would add PCIe latency and negate the GPU advantage