Profile a GPU Workload and Choose a Parallelism Strategy

Starting from a real GPU workload, explain how you would profile it, locate the performance bottleneck, and choose a parallelism strategy. Discuss tools you have actually used and why the chosen strategy can improve performance. ### Constraints & Assumptions Separate observed measurements from hypotheses. No profiler product, GPU topology, or workload size is fixed by the report; use tools appropriate to the environment and state the workload conditions. ### Clarifying Questions Is the goal latency, throughput, memory capacity, or cost? Is the critical path on CPU, storage, transfer, GPU kernels, or inter-device communication? How much independent work and data reuse exist? ### What a Strong Answer Covers A reproducible baseline, timeline and kernel-level evidence, resource constraints, parallelism choices, and controlled validation of the improvement. ### Follow-up Questions When do more devices fail to help? How do synchronization and load imbalance limit speedup? How would you distinguish a faster kernel from a faster application?