Benchmarks

Every benchmark from the upstream Nim reference under integration_tests/examples/ is auto-translated to Python DSL via tools/nim_to_dsl.py and lives in examples/. All compile through HIR/MIR cleanly; the smoke-tested ones also compile + run end-to-end on this box.

Canonical benchmarks

Benchmark	Relations	Rules	Strata	MIR steps	Notes
triangle	4	1	1	2	Classic 3-way self-join
tc	3	3	3	6	Transitive closure
sg	2	2	2	4	Same-generation
andersen	5	4	2	4	Pointer analysis
cspa	7	12	3	10	Context-sensitive points-to
galen	8	8	2	6	Ontology closure
crdt	22	24	17	39	CRDT replication semantics
polonius_test	38	38	32	68	Rust borrow-checker
ddisasm	39	23	11	30	Binary disassembly (needs --meta)
reg_scc	16	10	2	9	Register-SCC subquery of ddisasm
doop	76	84	16	60	Java points-to (needs --meta)

LSQB triangle variants

Benchmark	Notes
lsqb_q3_triangle	Basic 3-way triangle count
lsqb_q6_2hop	2-hop path
lsqb_q6_count	Count variant of q6
lsqb_q7_optional	Left-join / optional pattern
lsqb_q9_neg2hop	2-hop with negation
lsqb_triangle_count	Count-only triangle

Running one

# Synthetic, no input data:
python examples/run_benchmark.py triangle

# Needs a CSV dir:
python examples/run_benchmark.py tc --data /path/to/edges

# Needs a CSV dir + meta JSON:
python examples/run_benchmark.py doop \
    --data /path/to/doop_input_dir \
    --meta /path/to/batik_meta.json

# Compile only (no run):
python examples/run_benchmark.py galen --no-run

# Cap fixpoint for a sanity-check run:
python examples/run_benchmark.py polonius_test --data /path/to/data --max-iter 3

Each invocation prints one line per phase (DSL build → emit → compile → load → run) with wall-clock timings — useful when diagnosing where time is going on your box.

Regenerating from Nim

When upstream Nim sources change, regenerate every benchmark with:

for nim in integration_tests/examples/*/*.nim; do
  python tools/nim_to_dsl.py "$nim" --out examples/$(basename "$nim" .nim).py
done

The translator is deliberately noisy — it raises on any syntax it hasn’t been taught, so you’ll see exactly which benchmarks regressed. See tools/nim_to_dsl.py’s header for the full supported subset.