neuron-nki-profile-querying

You can query AWS Neuron kernel profiles using SQL or Python scripts to analyze instruction timing, engine utilization, and memory transfers, which helps identify specific performance bottlenecks. This requires existing profile data and the neuron-explorer tool for in-depth diagnostics.

Yes, you can calculate theoretical memory and compute bounds for Neuron kernel execution. By computing these bounds, you can determine whether data movement or compute operations are limiting performance before re-implementing your kernel.

To analyze Neuron hardware profiles, you need existing profile data, NEFF and NTFF files, and the neuron-explorer tool. You ingest the profile using the neuron-explorer view command, then use curl requests to retrieve summary and schema information.

You can trace and localize runtime inefficiencies such as redundant data transfers, idle engine time, or transposes by querying detailed profile tables. This allows you to pinpoint specific instructions or memory operations causing inefficiencies for targeted improvements.

The best way to analyze Tensor Engine utilization is by querying kernel execution profiles to extract engine utilization metrics. This reveals how effectively your neural network kernel uses the Tensor Engine and highlights if data movement is limiting performance.