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benchmark

Measure Z3 performance on a formula or file. Collects wall-clock time, theory solver statistics, memory usage, and conflict counts. Results are logged to z3agent.db for longitudinal tracking.

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SKILL.md
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benchmark
description
Measure Z3 performance on a formula or file. Collects wall-clock time, theory solver statistics, memory usage, and conflict counts. Results are logged to z3agent.db for longitudinal tracking.
Given an SMT-LIB2 formula or file, run Z3 with statistics enabled and report performance characteristics. This is useful for identifying performance regressions, comparing tactic strategies, and profiling theory solver workload distribution. # Step 1: Run Z3 with statistics Action: Invoke benchmark.py with the formula or file. Use `--runs N` for repeated timing. Expectation: The script invokes `z3 -st`, parses the statistics block, and prints a performance summary. A run entry is logged to z3agent.db. Result: Timing and statistics are displayed. Proceed to Step 2 to interpret. ```bash python3 scripts/benchmark.py --file problem.smt2 python3 scripts/benchmark.py --file problem.smt2 --runs 5 python3 scripts/benchmark.py --formula "(declare-const x Int)..." --debug ``` # Step 2: Interpret the output Action: Review wall-clock time, memory usage, conflict counts, and per-theory breakdowns. Expectation: A complete performance profile including min/median/max timing when multiple runs are requested. Result: If performance is acceptable, no action needed. If slow, try **simplify** to reduce the formula or adjust tactic strategies. The output includes: - wall-clock time (ms) - result (sat/unsat/unknown/timeout) - memory usage (MB) - conflicts, decisions, propagations - per-theory breakdown (arithmetic, bv, array, etc.) With `--runs N`, the script runs Z3 N times and reports min/median/max timing. # Step 3: Compare over time Action: Query past benchmark runs from z3agent.db to detect regressions or improvements. Expectation: Historical run data is available for comparison, ordered by recency. Result: If performance regressed, investigate recent formula or tactic changes. If improved, record the successful configuration. ```bash python3 ../../shared/z3db.py runs --skill benchmark --last 20 python3 ../../shared/z3db.py query "SELECT smtlib2, result, stats FROM formulas WHERE run_id IN (SELECT run_id FROM runs WHERE skill='benchmark') ORDER BY run_id DESC LIMIT 5" ``` # Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | formula | string | no | | SMT-LIB2 formula | | file | path | no | | path to .smt2 file | | runs | int | no | 1 | number of repeated runs for timing | | timeout | int | no | 60 | seconds per run | | z3 | path | no | auto | path to z3 binary | | debug | flag | no | off | verbose tracing | | db | path | no | .z3-agent/z3agent.db | logging database |
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