smalltt

Demo project for several techniques for high-performance elaboration with dependent types.

It is a complete rewrite of the old smalltt version which I wrote mostly in 2018-2019.

Table of Contents

• Overview
• Installation
• Language overview
• Design
  • Basics
    • NbE vs. naive evaluation
  • Contextual metavariables
  • Glued evaluation
    • On hash consing
  • Strict vs. lazy evaluation
  • Approximate conversion checking
  • Paired values
  • Eta-short meta solutions
  • Meta solution checking and quotation
  • Meta freezing and approximate occurs checking
• GHC-specific optimizations
  • Runtime system options
  • Custom exceptions
  • Data structures and libraries
• Benchmarks
  • Elaboration speed
  • Elaboration asymptotics
  • Raw conversion checking
  • Raw evaluation and normalization

Overview

This project serves as a demo for several techniques for high-performance elaboration with dependent types. I also include some benchmarks, which are intended to be as similar as possible across Agda, Lean, Coq, Idris 2 and smalltt.

You can skip to benchmarks if that's what you're after.

Smalltt is fast, however:

• Smalltt is not as nearly as fast as it could possibly be. A lot of tuning based on real-world benchmarking data is missing here, because there isn't any real-world code for smalltt besides the benchmarks that I wrote. A bunch of plausible optimizations are also missing.
• The primary motivation is to demonstrate designs that can plausibly scale up to feature-complete languages, still yield great performance there, and naturally support a lot more optimization and tuning than what's included here.

This project is not yet finalized. Code and documentation alike may be subject to change, cleanup, extension, or perhaps complete rewrite.

Installation

First, clone or download this repository.

Using stack:

• Install stack.
• Run stack install in the smalltt directory. If you have LLVM installed, use stack install --flag smalltt:llvm instead, that gives some performance boost.

Using cabal:

• Install cabal
• Run cabal v2-update.
• Run cabal v2-install in the smalltt directory. If you have LLVM, use cabal v2-install -fllvm instead.

Also make sure that the executable is on the PATH. On Linux-es, the stack install directory is $HOME/.local/bin, and the cabal one is $HOME/.cabal/bin.

Installation gets you the smalltt executable. You can find usage information after starting smalltt.

Language overview

Smalltt is a minimal dependent type theory implementation. Features:

• Type-in-type.
• Dependent functions.
• Agda-style implicit functions with named implicit arguments.
• Basic pattern unification.
• A distinguished top-level scope and local let-definitions.
• An executable which can load a single file at once, and lets us query top-level definitions in several ways.

See Basics.stt for introductory examples.

Design

Basics

The core design is based on Coquand's algorithm. This is sometimes called "elaboration with normalization-by-evaluation", or "semantic elaboration". This is becoming the de facto standard design of dependently typed elaboration nowadays, but it is in fact the preferred design for elaboration with any type system which includes binders and substitution in types. It is explained in detail in

• https://davidchristiansen.dk/tutorials/nbe/
• https://github.com/AndrasKovacs/elaboration-zoo

I give a short review. Dependent type checking involves execution of arbitrary functional programs at compile time. For executing functional programs, the standard practice (used in virtually all functional languages) is to have

1. Immutable program code, which may be machine code or interpreted code.
2. Runtime objects, consisting of constructors and closures.

The basic idea is to use the above setup during elaboration, with some extensions.

The elaborator takes as input raw syntax, and outputs core syntax, which corresponds to immutable program code. When necessary, we evaluate core syntax into semantic values (or values in short), which correspond to runtime objects, and indeed they represent function values as closures.

Evaluation and runtime values have to support a somewhat richer feature set than what is typical in functional languages. They must support open evaluation, that is, evaluation of programs which may contain free variables. This makes it possible to evaluate code under binders. In such code, free variables cause evaluation to get stuck. There are special values corresponding to stuck computations, which are called neutral values.

Neutral values make it possible to convert runtime values back to core syntax. This is called quoting. Quoting is used in elaboration whenever we need to serialize or store values. For example, since elaboration outputs core syntax, whenever we need to fill a hole in raw syntax, we plug the hole by converting a value to a core term by quoting.

Normalization by evaluation (NbE) means normalizing terms by first evaluating then quoting them. The kind of normal forms that we get can vary depending on the details of evaluation and quoting. In particular, it is not mandatory that NbE yields beta-normal terms.

Moreover, values support conversion checking. Type equality checking is required in pretty much any type checker. In dependently typed languages, types may include arbitrary programs, and equality checking becomes beta-eta conversion checking of values. At its simplest, this is implemented by recursively walking values. The "open" evaluation makes it possible to get inside closures during conversion checking, so we can check if two functions have beta-eta-convertible bodies.

NbE vs. naive evaluation

Elaboration with NbE can be contrasted to elaboration with "naive" evaluation. In this style, compile-time evaluation is performed by term substitution, which is far less efficient than NbE. In some implementations, naive substitution is still used because of its perceived simplicity. However, my experience is that NbE is significantly simpler to implement, and also easier to correctly implement, than capture-avoiding substitution. Furthermore, any attempt to use naive substitution in type checking necessitates additional optimizations, which add more complexity.

For example, Lean uses naive substitution in its kernel, but to recover acceptable performance it has to add extra machinery (memoization, free variable annotations on terms for skipping traversals during substitutions). This ends up being slower and more complicated than a straightforward NbE implementation.

In summary, term substitution should be avoided whenever possible in elaboration implementations. Sometimes it's necessary, but AFAIK only for more niche purposes in more feature-rich systems, and not in performance-critical tasks. Smalltt uses no substitution operation whatsoever, and we can go pretty far without one.

(Remark: cubical type theories are notorious for requiring substitution from the get go. It's an open research problem how to get rid of naive substitution there).

Contextual metavariables

Smalltt uses contextual metavariables. This means that every metavariable is a function which abstracts over the bound variables in its scope. Take the following surface code.

id : (A : U) → A → A
 = λ A x. x

id2 : (A : U) → A → A
 = λ A x. id _ x

When the elaborator hits the hole in id2, it fills it with a fresh metavariable which abstracts over A and x. The elaboration output is:

id : (A : U) → A → A
  = λ A x. x

?0 = λ A x. A

id2 : (A : U) → A → A
  = λ A x. id (?0 A x) x

Note that ?0 is a fresh top-level definition and the hole gets plugged with it. Smalltt's particular flavor of contextual metavariables puts metas in mutual top-level blocks in the elaboration output. Other setups are possible, including elaborating solved metas to local let-definitions, but those are significantly more complicated to implement.

Also, smalltt uses basic pattern unification for producing meta solutions. See this for a tutorial on the basics of contextual metavariables and pattern unification.

Smalltt does not try very hard to optimize the representation of contextual metas, it just reuses plain lambdas to abstract over scopes. For potential optimizations, see this Coq discussion: coq/coq#12526. As a result, basic operations involving metas are usually linear in the size of the local scope. My benchmarking showed that this is not a significant bottleneck in realistic user-written code, and we don't really have machine-generated code (e.g. by tactics) that could introduce pathologically sized local scopes.

Glued evaluation

The most basic NbE setup is not adequate for performance. The problem is that we need different features in conversion checking and in quoting:

• In basic conversion checking, we want to evaluate as efficiently as possible.
• In quoting, we want to output terms which are as small as possible. The reason is that, through metavariable solutions, the output of quoting is included in the overall elaboration output. So, if quoting returns full beta-normal terms, that reliably destroys performance, as normal forms tend to be extremely large.

The solution is to add control over definition unfolding to evaluation and quotation. We call the implementation glued evaluation, where the evaluator lazily computes two different values on each unfolding choice. In smalltt we have unfolding control only for top-level definitions. This simplifies implementation, and usually top-level scopes are vastly larger than local scopes, so we already capture the vast majority of size compression by only focusing on top-level unfolding.

See this file for a minimal demo of glued evaluation. In short, top-level variables are evaluated to values which represent lazy ("non-deterministic") choice between unfolding the definition, and not unfolding it. This has a noticeable constant overhead during evaluation but overall the trade-off is well worth it. Later, the quotation function has the choice of visiting either evaluation branches, or both, in which case as much as possible computation is shared between the branches.

When we need high-performance evaluation during conversion checking, we have it, and when we solve a metavariable, we are able to quote values to terms which are minimal with respect to top-level unfolding. This is also useful in error message reporting and interaction, where we want to be able to display small terms.

Only being able to control top-level unfolding is not quite sufficient for sophisticated interactive proving, but the technique here could be extended to richer unfolding control with modest additional overheads.

Importantly, we can make do with a single evaluator for all purposes, with fairly good performance. In contrast, Agda, Coq and Lean all have multiple evaluators, and in all cases only the slowest evaluators can be used without restrictions during elaboration. As we'll see in benchmarks, smalltt is robustly faster than all "slow" evaluators, and can be faster or slower than the Coq bytecode VM depending on workloads.

On hash consing

Usually by "hash consing" we mean a pervasive form of memoization, where certain objects are stored at most once in memory, and any new object construction goes through a table lookup to check if the object already exists. It is frequently mentioned as an optimization technique in typechecking. However, specifically in the context of dependent elaboration, it's not obviously desirable.

Hash consing alone is inadequate for eliminating size explosions. Hash consing merges duplicate objects to a single copy. But it does not handle beta-reduction at all, which is a major source of size explosion! For a simple example, using Peano naturals, it is easy to give a compact definition for oneMillion, involving arithmetic operations. But if I normalize oneMillion, I get a numeral which is incompressible by hash consing.

If I have something like a first-order term language, hash consing can be very effective. But in dependent type theory, we have higher-order programs with potentially explosive behavior, and it isn't hard to produce size explosions even in the presence of full hash-consing. Considering this, and the performance and complexity overhead of hash consing, I decide to skip it in smalltt.

Hash consing is better suited to more static data, like literals, or types in systems without type-level beta rules, such as simple type theory, Hindley-Milner or System F. In those cases, hash consing fully captures the compression which is possible by rewriting along conversion rules.

Strict vs lazy evaluation

In dependently typed elaboration, at least some laziness is essential, because some parts of the program may need to be evaluated, but we don't know anything about which parts, until we actually do the elaboration.

At the same time, laziness has significant overhead, so we should limit it to the necessary amount.

Smalltt has the following approach:

• Top-level and local definitions are lazy.
• We instantiate Pi types during elaboration with lazy values.
• Applications headed by top-level variables are lazy.
• Any other function application is call-by-value during evaluation.

The reasoning is the following. First, it does not make sense to have strict evaluation during infer/check, because that would cause the entire program to be evaluated during elaboration. Hence the laziness of definitions and Pi instantiation.

On the other hand, evaluation is really only forced by conversion checking. The bulk of the program is never forced by conversion checking, so we might as well make evaluation a bit stricter when it is actually forced, to make it faster.

However, glued evaluation mandates that top-level spines are lazily evaluated. So we keep that lazy, and otherwise have call-by-value function applications.

This seems to work well in practice. While there are some cases where it does superfluous work, in realistic code we still get plenty of laziness through let-definitions and top-level variables.

Approximate conversion checking

Approximate conversion checking means deciding conversion without computing all beta-redexes. It's an important feature in pretty much every major TT implementation. For example, if I again have oneMillion as a definition, checking that oneMillion is convertible to itself should immediately return with success, without unfolding the numeral.

• An important property here is whether a system permits approximate meta solutions. For example, if I unify f ?0 = f ?1 where f is a defined function, I might skip computing the f application, and pretend that f is injective, solving ?0 with ?1. But if f is actually a constant function, this causes ?0 and ?1 to be unnecessarily equated. AFAIK Coq and Lean both permit approximate solutions, and Agda does not.
• Another property is how optimistic the approximation algorithm is. A very optimistic algorithm might do the following: if we have identical defined head symbols on sides, first we try to unify spines, and if that fails we retry with unfolding. This algorithm expects that unifiable values are nearby, i.e. reachable after few reductions. The downside of unbounded optimism is that recursive backtracking can cause massive slowdown when unifiable values are not in fact near.

Smalltt

• Does not allow approximate meta solutions.
• Has bounded approximation: it only performs limited speculation, and switches to full reductions on failure.

Concretely, smalltt has three states in unification: "rigid", "flex" and "full". See unify in src/Unification.hs for details.

• "Rigid": this is the starting state. In this state we can solve metas, and can initiate speculation. Whenever we have the same top-level head symbol on both sides, we try to unify the spines in "flex" mode, if that fails, we unfold and evaluate the sides, and unify them in "full" mode. We stay in "rigid" mode when we recurse under canonical type and term formers.
• "Flex": in this state we cannot solve metas, every situation which requires a meta solution fails. Moreover, we cannot unfold any top-level definition; if we have identical defined head symbols, we can recurse into spines, but any situation which requires unfolding also causes failure.
• "Full": in this state we can solve metas, and we always immediately unfold any defined symbol.

Example. We unify cons oneMillion (cons oneMillion nil) with itself. Assume that cons and nil are rigid term formers for lists. We start in rigid mode, which recurses under the cons-es, and tries to unify oneMillion with itself twice. Both cases succeed speculatively, because head symbols match and oneMillion is applied to zero arguments.

Example. We unify const true true with const true false, where const is a top-level definition. We start in rigid mode, and since we have const head on both sides, we try to unify spines in flex mode. This fails, since true /= false. So we unfold the const-s, and unify sides in full mode.

In short, smalltt unification backtracks at most once on any path leading to a subterm ("sub-value" actually, since we recurse on values).

We could have a large number of different speculative algorithms. A natural generalization to smalltt is to parametrize the "rigid" state with the number of shots we get at speculation (smalltt has just one shot). We start in "rigid N" state, and when a speculative (flex) spine unification fails, we continue in "rigid (N-1)", and "rigid 0" corresponds to the "full" state. I had this briefly but did not find much difference in the benchmarks compared to the one-shot speculation. Alternatively, we could parameterize the "flex" mode with a number of allowed unfoldings (currently unfolding is not allowed).

I haven't yet done benchmarking on larger, more realistic codebases. The point is that the current system is compatible with a large number of approximate conversion checking algorithms, so we could adapt it based on more real-world performance data. The main limitation is that we can only suspend top-level unfoldings, and local let-s and immediate local beta-redexes are always computed.

Paired values

In infer/check and in unification, instead of using plain values, we use pairs of values, named data G = G {g1 :: Val, g2 :: Val} in the source. Hence, unify takes two G-s, and infer returns a G for inferred type.

In G, the two values are always convertible, but the first value is always the least reduced available version, and the second one is potentially more reduced.

For example, if we do check-ing, the checking type can be headed by a top-level definition, so we have to compute it until we hit a rigid head symbol, to see whether it's a Pi type. This computation yields a new value which is more reduced than what we started with. But we don't want to throw away either of these values! The original version is usually smaller, hence better for printing and meta solutions, the forced version is more efficient to compute with, since we don't want to redo the same forcing later.

Eta-short meta solutions

We prefer to get meta solutions which are as eta-short as possible. Eta-expansion increases code size and makes evaluation of code slower.

In the standard implementation of syntax-directed function eta-conversion checking, we do the following:

1. If we have lambdas on both sides, we recurse under binders.
2. If we have a lambda only on one side, we recurse under that lambda, and apply the other side to a fresh variable.
3. We only attempt solving metas after we've checked case 2. For example, if we have a lambda on one side, and a meta-headed value on the other side, first we perform eta-expansion according to step 2.

In smaltt, this is slightly modified to allow eta-short meta solutions. If we have a meta on one side, and a non-meta on the other side, we immediately attempt a solution. However, this can fail if the sides are eta-convertible. For example, trying to solve ?0 with λ x. ?0 x fails because ?0 occurs rigidly in the solution. So in case of solution failure, we just retry with full eta expansion. Such failure seems to be very rare in practice, so we almost always get the eta-short solutions. This is the place where we retry after a failed eta-short solution.

Furthermore, we do additional eta-contraction in pattern unification. We try to contract meta spines, for example ?0 x y z = ?1 x y z is contracted to ?0 = ?1. This is also used in Coq. We have to be careful though not to change pattern conditions by contraction, e.g. not remove non-linear bound vars by contraction.

Eta-short solutions are also important for preserving top-level unfoldings. For example, assume a function f : Nat → Nat defined as a lambda λ x. t, where t can be a large definition. If I unify ?0 = f, the eta-long unification would solve ?0 := λ x. t x, while the eta-short version can preserve the f unfolding, and solve simply as ?0 := f.

Meta solution checking and quotation

Let's look now at the actual process of generating meta solutions. In basic pattern unification, we have problems like ?m x₁ x₂ ... xₙ = rhs, where ?m is a meta, xᵢ are distinct bound variables, and rhs is a value. We aim to quote rhs to a solution term, and at the same time check occurs & scoping conditions on it.

• Scoping: the only bound vars rhs can depend on are the xᵢ vars in the spine.
• Occurs: ?0 cannot occur in rhs (we assume that rhs is not headed by ?0).

If both conditions hold, then it is possible to quote rhs to some t term which depends only on xᵢ bound variables, so that λ x₁ x₂ ... xₙ. t is a well-formed term. In the actual implementation we use a variable renaming structure to map De Bruijn levels in rhs to the correct De Bruijn indices in the output.

The naive implementation beta-normalizes rhs while quoting, which we want to avoid. In smalltt the rhs is quoted without unfolding any top-level definitions or any previously solved meta. However, this is not entirely straightforward, because the rhs conditions should be still checked modulo full beta-reductions.

We have three different quotation modes, somewhat similarly to what we have seen in unification. See flexQuote, rigidQuote and fullCheck in src/Unification.hs.

• "rigid": the starting mode. We stay in rigid mode when going under canonical type/term formers. Illegal var occurrences cause an error to be thrown. When we hit an unfolding, we recurse into the spine in flex mode, if that returns a possibly invalid term, we check the unfolding in full mode. If that succeeds, we learn that the term is actually valid, and return it.
• "flex": this mode returns a boolean flag alongside a term. A true flag means that the term is definitely valid, a false means that it is possibly invalid. Illegal var occurrences cause a special Irrelevant term to be returned along with a false flag.
• "full": this mode does not return any term, it just fully traverses the value and throws an error on any illegal var occurrence.

The overall result of this algorithm is that top definitions are never unfolded in any meta solution, but we check validity up to full beta-reduction. Recall the ?0 = const {Bool}{Bool} true y example. This yields the ?0 solution const {Bool}{Bool} true Irrelevant. Note that the Irrelevant part disappears during evaluation.

In unification, Irrelevant immediately unifies with anything, since it signals that we are in an irrelevant evaluation context.

It would be better in the previous example to solve ?0 with true. Smalltt does not bother with performing unfolding for code optimization, but it certainly could; the primary goal is to demonstrate the infrastructure where we have the freedom to unfold in different ways. Additional optimization passes can take advantage of the preserved top-level unfoldings.

Meta freezing and approximate occurs checking

Freezing metas means that at certain points during elaboration we mark unsolved metas as unsolvable. This may be used as a performance optimization and/or a way to enforce meta scoping. All major systems use at least some meta freezing. The absence of freezing would mean that metas are solvable across the whole program, across module hierarchies.

Smalltt freezes metas like Agda does: a top-level definition together with its optional type annotation constitutes the elaboration unit where fresh metas are solvable (but in Agda such blocks can be greatly enlarged through mutually recursive definitions).

This enforces a scoping invariant: metas can be grouped to mutual blocks before each top-level definition. Within a mutual block metas can refer to each other freely, but outside of the block they can only refer to in-scope top defs and metas in previous blocks.

An active meta is in the current meta block. It can be solved or unsolved, and an unsolved active meta may become solved.

A frozen meta is in a previous meta block. A frozen unsolved meta cannot be solved.

This yields a major optimization opportunity in meta occurs checking: an active unsolved meta can only occur in the solution of an active meta, but no other top-level definition! We exploit this in rigid and flex solution quoting. There, we only look inside solutions of active metas, to do approximate occurs checking.

For example, assume we're checking for ?m occurrences, and we hit ?n spine, where ?n is a solved active meta. It is not enough to check spine, we also need to look into the ?n solution. We do this by simply recursively walking the term solution of ?n, which may lead to looking into solutions of other active metas. Here we employ a very simple caching mechanism: we only visit each active solved meta at most once. So the amount of work done in approximate occurs checking is limited by the total size of all active meta solutions.

As a result, smalltt is able to very quickly check the classic nested pair example:

dup : {A} → A → Pair A A
 = ...

pairTest =
  let x0  = dup U ;
  let x1  = dup x0;
  let x2  = dup x1;
  ...
  x20

At each dup, the normal form of the inferred A type doubles. In smalltt this benchmark is technically quadratic, since at each dup we search all previous active solved metas. But these meta solutions are all tiny, they are of the form ?n := Pair ?(n-1) ?(n-1). This takes exponential time in Agda, Coq and Lean, although in Lean the profiling shows that "elaboration" is not exponential, only "compilation" is. See elaboration asymptotics.

More sophisticated caching mechanisms are plausible and probably desirable. For better UX, it could make sense to combine smarter caching with more relaxed meta freezing behavior, like allowing metas to be active within a single module.

GHC-specific optimizations

Smalltt includes some low-level GHC-specific optimizations. These almost certainly make the code less readable. I included them because

• A very important goal was to have an implementation in any language which robustly beats all existing systems in elaboration speed. Hence I did not want to leave too much performance on the table. Having concise code is also a goal, but I care more about the complexity of the underlying algorithms, and less about the size of supporting code. The optimization boilerplate in smalltt can be easily ignored when we want to look at the core algorithms.
• I wanted to at least try some GHC-specific optimizations, to get an idea about their impact in the first place. Some of these optimizations turned out to have modest impact, but all of them help to some degree.

Runtime system options

Setting RTS options is important and often overlooked. The performance gains from the right settings can be easily 30-50%. The default arena size in GHC (1MB or 4MB starting from GHC 9.2) is very tiny compared to typical RAM sizes. In smalltt I set the default RTS options to be -A64M -N8. This means that effective arena size is 8 * 64MB = 512MB, so smalltt allocates in 512MB chunks. Is this wasteful? RAM sizes below 8GB are getting increasingly rare; 512MB is 1/16th of that, and 1/32nd of 16GB. If we can trade RAM for performance, while still keeping the risk of running out of RAM very low, then we should do it. RAM exists to be used, not to just sit there.

One of the main reasons why smalltt is implemented in GHC Haskell is the RTS performance, which is overall great (the other reason is my prior experience in GHC optimization). I plan to update my old normalization benchmarks at some point; even there GHC performs well, but my newer unstructured benchmarking with newer GHC versions indicates yet more GHC advantage.

Custom exceptions

This is the dirtiest trick here. I use custom catching and throwing functions, to avoid the overhead of the standard fingerprint mechanism which ensures type safety. See Exception# in src/Exceptions.hs. The idea is that my own primitive Exception# type includes the standard SomeException constructor, but has another one for my own custom exceptions. As a result, I can catch and throw standard exceptions, but also custom exceptions which have zero fingerprint overhead. This relies on the ability to silently and unsafely cast the SomeException constructor between the standard Exception type and my Exception# type. It works because the memory representation is the same.

I had an old benchmark where custom exceptions had roughly 1/5 the overhead of standard exceptions. I haven't yet benchmarked both versions in smalltt, I really should.

Data structures and libraries

Hash table

src/SymTable.hs is a custom mutable hash table implementation, keyed by source position spans. The reason for writing this is that I have had performance problems with hashtables, the primary mutable hashtable package, where it was even outperformed by the immutable Data.HashMap. However, this was a few years ago, so I should benchmark my version against alternatives.

I'm also using a custom hash function on bytestrings, which is mostly based on the non-AES "fallback" hasher in ahash.

I intend to release in the future a generic variant of my hashtable (which is not specialized to source span keys) along with my hash functions.

Libraries

I use the following:

• primdata: a low-level, minimum-overhead array & mutable reference library. It can be viewed as a replacement for primitive with a significantly different API.
• dynamic-array: a small dynamic array library, built on top of primdata.
• flatparse: a high-performance parser combinator library. It is ridiculously faster than all of the parsec libraries. The old smalltt versions before the rewrite used megaparsec, which was about 40 times slower. Parsing speed is now typically 2-3 million LOC/s on my system.

Benchmarks

All files used in benchmarking are available in bench. The following programs were used.

• agda 2.6.2 with options -vprofile:7 +RTS -M10G.
• coq 8.13.2, used as time coqtop -l FILE -batch -type-in-type -time, or dropping the last -time options when benchmarking elaboration of large files.
• Lean 4.0.0 nightly 2021-11-20, commit babcd3563d28. Used as time lean FILE --profile.
• smalltt: in elaboration benchmarks I use timings for :r instead of :l; reloading tends to be 20-30% faster, presumably because the RTS is "warmed up" by previous heap allocations. I find this to be more representative of typical usage where we mostly reload files we're actively working on.
• idris: I use the version in this pull request which fixes a quadratic parsing bug. Command is time idris2 -c FILE.

System: Intel 1165G7 CPU, 16GB 3200 MT/s RAM, CPU set to run at 28 W power draw.

Abbreviations:

• SO: stack overflow.
• TL: "too long", I did not have enough patience.
• OOM: out of memory.

All time figures are in seconds.

Benchmark results are currently in rather crappy hand-pasted markdown tables, I plan to have nicer graphs here.

There are some benchmark entries marked as N/A. In these cases I haven't yet been able to reproduce the exact benchmarks in a given system. There are also some stack overflows that could be avoidable, but I could not appropriately set stack settings. Pull requests are welcome to fix these!

Elaboration speed

• stlc: Church-coded simply-typed lambda calculus plus some internal definitions. Fairly heavy in terms of sizes of types and the number of metavariables. Comes in 5k and 10k LOC versions, where everything is renamed and copied many times.
• stlcLessImpl: a lighter version, with no implicit arguments inside stlc algebras. This works in Coq, while the heavier version does not, because Coq does not support higher-order implicit arguments (implicit function types are not first-class).
• stlcSmall: an even lighter version which only includes variables, lambdas and applications in the object language. For fun, I also test files with one million lines of code. I don't include these files in the repo, because they're around 50MB each. They can be generated by running stlcSmall1M functions from src/GenTestFiles.hs.
• The different systems do somewhat different kinds of work. Smalltt and coqtop only elaborate input, while Agda and Idris do module serialization, and Lean apparently does some compilation according to its profiling output. However, the numbers should be still indicative of how much we have to wait to have the entire input available for interactive use.
• Note: Agda 2.6.2 has a parsing blowup issue on large files: agda/agda#5670. So only stlc, stlcLessImpl, and stlcSmall are really indicative of elaboration performance.

Elaboration asymptotics

See the asymptotics files.

• idTest: id id ... id, 40 times iterated.
• pairTest: 30 times nested pairs using local let.
• vecTest: length-indexed vector with 960 elements.

We separately consider elaboration time and total command time, because serialization often takes a quadratic hit on vecTest.

• smalltt is also quadratic on vecTest! But it's fast enough to be a non-issue. The quadratics comes from the occurs checking, which visits a linear number of active metas for each cons cell. Making it overall linear is possible, but it would require smarter meta occurs caching.
• Lean elaboration is actually linear on pairTest, but the task itself does not finish, because the "compilation" phase is exponential.

Raw conversion checking

See the conv_eval files.

• NatConv: conversion checking Church Peano numerals of given size
• TreeConv: conversion checking complete Church binary trees of given depth
• TreeConvM: same but sides contain unsolved metas.

• I Coq I haven't yet been able to find a way to increase stack sizes enough, for the Nat benchmark.
• Agda, Coq and Lean all have more aggressive approximate conversion checking than smalltt. Lean can shortcut all tree conversion tasks. For the TreeConvM tasks, this requires approximate meta solutions. It's apparent that Agda does not do such solutions.
• Agda performance degrades sharply when we throw metas in the mix.

Raw evaluation and normalization

See the conv_eval files again.

• ForceTree : fold over a binary tree with Boolean conjunction
• NfTree : normalize a tree

• Agda, Lean and Idris 2 NfTree is N/A because there is no way to only force the normal forms, without doing printing or conversion checking.
• Coq vm_compute is extremely strong in ForceTree, which is a fairly lightly allocating workload. I note that smalltt with glued evaluation disabled would be 2x faster here, but that would be still just the third of Coq VM performance.
• On the other hand, smalltt is faster in normalization, a more allocation-heavy task. I attribute this to superior RTS performance.