← Anthropic Interview Insights
Start by establishing a reliable baseline and profiling the kernel to identify bottlenecks. Then iteratively apply targeted optimizations, validating correctness and measuring speedup with the cycle-accurate simulator. Prioritize changes by expected impact and effort, and document trade-offs.
Pro tip: Always verify that optimizations preserve numerical accuracy and handle edge cases; a faster kernel that produces slightly different results is worthless. Use the simulator's detailed metrics to guide decisions rather than guessing.
Run the original kernel in the simulator to get baseline cycle count and performance metrics. Profile to identify hotspots, bottlenecks, and resource utilization.
Analyze profiling data to find inefficiencies such as memory stalls, low ILP, or poor cache usage. Brainstorm potential optimizations like loop transformations, vectorization, or algorithmic changes.
Apply optimizations one at a time, ensuring correctness by comparing outputs with the baseline. Measure performance impact using the simulator and keep only changes that yield speedup.
Repeat profiling and optimization cycles, combining successful changes and exploring further improvements. Consider trade-offs between different optimizations.
Perform a final correctness check and measure overall speedup. Document the process, results, and any remaining bottlenecks or future work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around a two-pronged strategy: first, describe a rigorous correctness validation process using automated tests, invariants, and differential checks; second, explain how you guard against simulator overfitting by validating against real-world data, using held-out scenarios, and monitoring for distribution shift. Emphasize that you treat the simulator as a tool, not the ground truth, and continuously seek independent verification.
Pro tip: Mention that you keep a 'golden' set of real-world traces or manually verified cases that the simulator never sees, and you run them after every change to catch overfitting early. This shows you think beyond the simulator and value ground truth.
Before any optimization, define and record the expected behavior using a comprehensive test suite, including unit tests, integration tests, and property-based tests. This baseline serves as the reference for all future changes.
After each change, run the full test suite and compare outputs against the baseline. Use continuous integration to enforce that no regressions slip through, and add new tests for any edge cases discovered.
Employ differential testing against a reference implementation or previous version, and check invariants that must always hold. This catches subtle bugs that unit tests might miss.
Regularly test on real-world data or held-out scenarios not used during optimization. Compare simulator results with real-world outcomes to detect overfitting and distribution shift.
Continuously monitor performance in production or realistic environments, and be ready to roll back if discrepancies arise. Use feedback to refine both the simulator and the optimization approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Data layout first, then reducing memory traffic, then instruction scheduling.
Start by clarifying the context: what system, what metrics, and what constraints. Then propose three optimizations that target the biggest bottlenecks first, explaining how you'd measure impact and why they outrank other options. Emphasize data-driven prioritization and trade-offs.
Pro tip: Always tie optimizations to measurable business or user impact—interviewers at Anthropic care about reasoning and evidence, not just technical cleverness. Mention that you'd validate assumptions with profiling before committing to any optimization.
Ask questions to understand the system, current performance, goals, and constraints (e.g., latency, throughput, cost, maintainability).
Explain how you'd profile or measure to find the top bottlenecks, such as CPU, memory, I/O, or algorithmic complexity.
List three specific optimizations, each targeting a high-impact area, and briefly describe how you'd implement them.
Compare the three against other possible optimizations, using criteria like expected impact, effort, risk, and alignment with goals.
Describe how you'd measure the success of each optimization and iterate if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.