ba8c2ed4ee
This PR adds `simpArrowTelescope`, a simproc that simplifies telescopes of non-dependent arrows (p₁ → p₂ → ... → q) while avoiding quadratic proof growth. When using `Expr.forallE` to represent nested implications, each nesting level bumps de Bruijn indices in subterms, destroying sharing even with hash-consing. For example, a free variable `x` gets different de Bruijn representations at each depth, causing proof terms to grow. `simpArrowTelescope` works by: - Converting arrows to `Arrow p q` (a definitional wrapper) - Simplifying each component - Converting back to `→` form Since `Arrow` arguments are not under binders, subterms remain identical across nesting levels and can be shared. The `simp_4` benchmark demonstrates the improvement: With `forallE`: ~160ms, proof_size ≈ 173k With `Arrow`: ~43ms, proof_size ≈ 16k Tradeoff: `simpArrowTelescope` misses simplifications that depend on the arrow structure (e.g., `p → p` to `True`), since post-methods aren't applied to intermediate arrows. Thus, it is not used by default. to use it, one has to set `simpArrowTelescope` as a `pre`-method. |
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| .. | ||
| inundation | ||
| mergeSort | ||
| qsort | ||
| sym | ||
| .gitignore | ||
| accumulate_profile.py | ||
| arith_eval.ml | ||
| big_beq.lean | ||
| big_beq_rec.lean | ||
| big_deceq.lean | ||
| big_deceq_rec.lean | ||
| big_do.lean | ||
| big_match.lean | ||
| big_match_nat.lean | ||
| big_match_nat_split.lean | ||
| big_match_partial.lean | ||
| big_omega.lean | ||
| big_struct.lean | ||
| big_struct_dep.lean | ||
| binarytrees.ghc-6.hs | ||
| binarytrees.lean | ||
| binarytrees.lean.args | ||
| binarytrees.lean.expected.out | ||
| binarytrees.ocaml-2.ml | ||
| binarytrees.st.hs | ||
| binarytrees.st.lean | ||
| binarytrees.st.mlton-2.sml | ||
| binarytrees.st.sml | ||
| binarytrees.st.swift | ||
| binarytrees.swift | ||
| binarytrees5.ml | ||
| binarytrees5_multicore.ml | ||
| bv_decide_inequality.lean | ||
| bv_decide_large_aig.lean | ||
| bv_decide_mod.lean | ||
| bv_decide_mul.lean | ||
| bv_decide_realworld.lean | ||
| bv_decide_rewriter.lean | ||
| channel.lean | ||
| charactersIn.lean | ||
| compile.sh | ||
| const_fold.hs | ||
| const_fold.lean | ||
| const_fold.lean.args | ||
| const_fold.lean.expected.out | ||
| const_fold.ml | ||
| const_fold.sml | ||
| const_fold.swift | ||
| cross.yaml | ||
| dag_hassorry_issue.lean | ||
| dag_hassorry_issue.lean.args | ||
| dag_hassorry_issue.lean.expected.out | ||
| deriv.hs | ||
| deriv.lean | ||
| deriv.lean.args | ||
| deriv.lean.expected.out | ||
| deriv.ml | ||
| deriv.sml | ||
| deriv.swift | ||
| ex-50-50-1.leq | ||
| flake.lock | ||
| flake.nix | ||
| full-stdlib.exec.yaml | ||
| ghc-gc.py | ||
| hashmap.lean | ||
| identifier_completion.lean | ||
| identifier_completion_didOpen.log | ||
| identifier_completion_initialization.log | ||
| identifier_completion_runner.lean | ||
| ilean_roundtrip.lean | ||
| iterators.lean | ||
| lean-gc.py | ||
| liasolver.lean | ||
| liasolver.lean.args | ||
| liasolver.lean.expected.out | ||
| Makefile | ||
| mlkit-gc.py | ||
| mut_rec_wf.lean | ||
| nat_repr.lean | ||
| nat_repr.lean.args | ||
| nat_repr.lean.expected.out | ||
| ocaml-gc.py | ||
| omega_stress.lean | ||
| parser.lean | ||
| perf.py | ||
| phashmap.lean | ||
| qsort.hs | ||
| qsort.lean | ||
| qsort.lean.args | ||
| qsort.lean.expected.out | ||
| qsort.ml | ||
| qsort.sml | ||
| qsort.swift | ||
| rbmap.hs | ||
| rbmap.lean | ||
| rbmap.lean.args | ||
| rbmap.lean.expected.out | ||
| rbmap.ml | ||
| rbmap.sml | ||
| rbmap.swift | ||
| rbmap2.lean | ||
| rbmap3.lean | ||
| rbmap500k.lean | ||
| rbmap_checkpoint.hs | ||
| rbmap_checkpoint.lean | ||
| rbmap_checkpoint.lean.args | ||
| rbmap_checkpoint.lean.expected.out | ||
| rbmap_checkpoint.ml | ||
| rbmap_checkpoint.sml | ||
| rbmap_checkpoint.swift | ||
| rbmap_checkpoint2.lean | ||
| rbmap_checkpoint2.sml | ||
| rbmap_checkpoint_cpp_lean3.cpp | ||
| rbmap_checkpoint_cpp_std.cpp | ||
| rbmap_cpp_lean3.cpp | ||
| rbmap_cpp_std.cpp | ||
| rbmap_fbip.lean | ||
| rbmap_library.lean | ||
| README.md | ||
| reduceMatch.lean | ||
| report.py | ||
| riscv-ast.lean | ||
| run.sh | ||
| server_startup.lean | ||
| server_startup.log | ||
| sigmaIterator.lean | ||
| simp_arith1.lean | ||
| simp_bubblesort_256.lean | ||
| simp_congr.lean | ||
| simp_local.lean | ||
| simp_subexpr.lean | ||
| speedcenter.exec.velcom.yaml | ||
| speedcenter.yaml | ||
| states35.lean | ||
| test_single.sh | ||
| treemap.lean | ||
| unionfind.lean | ||
| unionfind.lean.args | ||
| unionfind.lean.expected.out | ||
| unionfind_clean.lean | ||
| watchdogRss.lean | ||
| workspaceSymbols.lean | ||
| workspaceSymbolsNewRanges.lean | ||
Lean Benchmark Suites
This folder contains multiple small Lean programs for benchmarking used by two separate benchmark suites based on the temci benchmarking tool:
- The light-weight "Speedcenter" suite benchmarks the current build of Lean. It can be used for quick comparisons on the cmdline and powers the Lean Speedcenter website.
- The heavy-weight "Cross" suite benchmarks multiple Lean configurations and other functional compilers against each other and generates CSV and HTML reports from that. It was created for the paper "Counting Immutable Beans - Reference Counting Optimized for Purely Functional Programming" (IFL19).
Speedcenter Suite
Requirements:
- A local Lean build in
../../build/release. Build at least thebintarget. - temci. Using Nix, open a nix-shell in the project
root directory to add a compatible version to your PATH. Alternatively, try
pip3 install git+https://github.com/parttimenerd/temci.git.
To execute the suite and save the results in base.yaml, run (in this folder)
temci exec --config speedcenter.yaml --out base.yaml
Other interesting exec flags:
- use
--runs Nto modify the default number of 10 runs per benchmark - use
--included_blocks fastto excluded slow benchmarks like the stdlib benchmark. You can replacefastwith any benchmark name or label inspeedcenter.exec.yaml.
If you have multiple saved result files, you can compare them with
temci report --config speedcenter.yaml report1.yaml report2.yaml ...
Cross Suite
We recommend using Nix for building/obtaining all Lean variants and used compilers in a reproducible way. After installing Nix, running the benchmarks is as easy as
nix develop
make
This will record 50 runs for each benchmark configuration (this can be changed with runs in cross.yaml),
generate results in report_lean.csv and report_cross.csv, and print them to stdout in a tabulated format.
It will also generate HTML reports in report/ comparing the time-based benchmarks.
In order to reduce noise in the benchmarking data, you may instead want to try calling make inside a
temci shell:
temci short shell --sudo --preset usable --cpuset_active make
Using root powers, this will temporarily configure your machine similarly to the LLVM benchmarking recommendations and move all your other processes to a single CPU core.