Profile a GPU Workload and Choose a Parallelism Strategy
AMDSoftware Engineer · OnsiteLast updated
Profile a GPU workload from its critical path to kernel counters, then choose parallelism based on independence, memory capacity, communication, and measured end-to-end gains.
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?
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?
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