Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

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May 2026

Summary

Anthropic system design round, one big open-ended question about GPU kernel profiling methodology. No coding, just a long whiteboard-style conversation about how you'd actually run experiments when all you have is a simulator spitting out cycle counts.

Questions Asked (1)

You have a kernel simulator that outputs cycle counts, IPC, stall reasons, occupancy, and memory bandwidth. Walk through a rigorous profiling and experimentation plan: how do you set a baseline, control variables, collect data, reduce variance, decide when to stop, attribute speedup to specific changes, and verify correctness after each iteration.

A/B Testing & ExperimentationSystem DesignTechnical Trade-offs

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Suggested Approach

Structure your answer around a scientific method: define a baseline, isolate variables, measure with statistical rigor, and iterate until improvements are both significant and correct. Emphasize that profiling is not just about collecting data but about forming and testing hypotheses to attribute performance changes.

Pro tip: Always establish a performance model (e.g., roofline or bottleneck analysis) before diving into experiments; this guides which variables to control and prevents chasing noise. Also, automate your experiment harness early to ensure reproducibility and reduce manual errors.

1. Establish a Baseline and Define Metrics

Run the simulator with a fixed, representative workload and record all metrics (cycle counts, IPC, stall reasons, occupancy, memory bandwidth). Ensure the baseline is stable by running multiple times and reporting variance.

2. Control Variables and Design Experiments

Identify independent variables (e.g., cache size, scheduling policy) and hold all others constant. Use a factorial design or one-factor-at-a-time approach to isolate effects, and document each change.

3. Collect Data with Statistical Rigor

Run each configuration multiple times to account for non-determinism. Use summary statistics (mean, median, standard deviation) and consider confidence intervals to assess significance.

4. Reduce Variance and Decide When to Stop

Increase sample size or use variance reduction techniques (e.g., common random numbers, paired runs) until the margin of error is acceptable. Stop when the effect size is stable and further runs don't change conclusions.

5. Attribute Speedup and Verify Correctness

Use differential profiling (e.g., compare stall reasons before/after) to attribute gains to specific changes. After each iteration, run correctness checks (e.g., output comparison, invariant tests) to ensure no regressions.

Key Points to Mention

Use of performance counters and bottleneck analysis (e.g., roofline model) to guide hypotheses.
Importance of controlling for confounding variables like workload characteristics and simulator settings.
Statistical methods for variance reduction: multiple runs, confidence intervals, and hypothesis testing.
Techniques for attributing speedup: differential profiling, A/B testing, and ablation studies.
Correctness verification: unit tests, golden outputs, and invariant checks after each change.
Automation and reproducibility: scripting the experiment pipeline and versioning configurations.

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