Diagnose a PyTorch Training Bottleneck from Profiler Traces
Company: Imc
Role: Hardware Engineer
Category: Machine Learning
Difficulty: easy
Interview Round: Onsite
Overview: Use PyTorch profiler traces to distinguish input starvation, CPU launch overhead, synchronization, CUDA kernels, memory limits, and distributed communication. The solution ties each optimization to a causal code path, predicted trace change, end-to-end throughput, and model-correctness checks.
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