The Glasgow Haskell Compiler (GHC) started as part of an academic research project funded by the UK government at the beginning of the 1990s, with several goals in mind:

To make freely available a robust and portable compiler for Haskell that generates high performance code;

To provide a modular foundation that other researchers can extend and develop;

To learn how real programs behave, so that we can design and build better compilers.

GHC is now over 20 years old, and has been under continuous active development since its inception. Today, GHC releases are downloaded by hundreds of thousands of people, the online repository of Haskell libraries has over 3,000 packages, GHC is used to teach Haskell in many undergraduate courses, and there are a growing number of instances of Haskell being depended upon commercially.

Over its lifetime GHC has generally had around two or three active developers, although the number of people who have contributed some code to GHC is in the hundreds. While the ultimate goal for us, the main developers of GHC, is to produce research rather than code, we consider developing GHC to be an essential prerequisite: the artifacts of research are fed back into GHC, so that GHC can then be used as the basis for further research that builds on these previous ideas. Moreover, it is important that GHC is an industrial-strength product, since this gives greater credence to research results produced with it. So while GHC is stuffed full of cutting-edge research ideas, a great deal of effort is put into ensuring that it can be relied on for production use. There has often been some tension between these two seemingly contradictory goals, but by and large we have found a path that is satisfactory both from the research and the production-use angles.

In this chapter we want to give an overview of the architecture of GHC, and focus on a handful of the key ideas that have been successful in GHC (and a few that haven't). Hopefully throughout the following pages you will gain some insight into how we managed to keep a large software project active for over 20 years without it collapsing under its own weight, with what is generally considered to be a very small development team.

5.1. What is Haskell? Haskell is a functional programming language, defined by a document known as the "Haskell Report" of which the latest revision is Haskell 2010 [Mar10]. Haskell was created in 1990 by several members of the academic research community interested in functional languages, to address the lack of a common language that could be used as a focus for their research. Two features of Haskell stand out amongst the programming languages crowd: It is purely functional. That is, functions cannot have side effects or mutate data; for a given set of inputs (arguments) a function always gives the same result. The benefits of this model for reasoning about code (and, we believe, writing code) are clear, but integrating input/output into the purely functional setting proved to be a significant challenge. Fortunately an elegant solution in the form of monads was discovered, which not only allowed input/output to be neatly integrated with purely functional code, but introduced a powerful new abstraction that revolutionised coding in Haskell (and subsequently had an impact on other languages too).

It is lazy. This refers to the evaluation strategy of the language: most languages use strict evaluation in which the arguments to a function are evaluated before the function is called, whereas in Haskell the arguments to a function are passed unevaluated, and only evaluated on demand. This aspect of Haskell also has benefits for reasoning about programs, but more than anything else serves as a barrier to prevent the leakage of impure non-functional features into the language: such features fundamentally cannot work in conjunction with lazy semantics. Haskell is also strongly-typed, while supporting type inference which means that type annotations are rarely necessary. Those interested in a complete history of Haskell should read [HHPW07].

5.2. High-Level Structure At the highest level, GHC can be divided into three distinct chunks: The compiler itself. This is essentially a Haskell program whose job is to convert Haskell source code into executable machine code.

The Boot Libraries. GHC comes with a set of libraries that we call the boot libraries, because they constitute the libraries that the compiler itself depends on. Having these libraries in the source tree means that GHC can bootstrap itself. Some of these libraries are very tightly coupled to GHC, because they implement low-level functionality such as the Int type in terms of primitives defined by the compiler and runtime system. Other libraries are more high-level and compiler-independent, such as the

type in terms of primitives defined by the compiler and runtime system. Other libraries are more high-level and compiler-independent, such as the Data.Map library. The Runtime System (RTS). This is a large library of C code that handles all the tasks associated with running the compiled Haskell code, including garbage collection, thread scheduling, profiling, exception handling and so on. The RTS is linked into every compiled Haskell program. The RTS represents a significant chunk of the development effort put into GHC, and the design decisions made there are responsible for some of Haskell's key strengths, such as its efficient support for concurrency and parallelism. We'll describe the RTS in more detail in Section 5.5. In fact, these three divisions correspond exactly to three subdirectories of a GHC source tree: compiler , libraries , and rts respectively. We won't spend much time here discussing the boot libraries, as they are largely uninteresting from an architecture standpoint. All the key design decisions are embodied in the compiler and runtime system, so we will devote the rest of this chapter to discussing these two components. Code Metrics The last time we measured the number of lines in GHC was in 1992 ("The Glasgow Haskell compiler: a technical overview", JFIT technical conference digest, 1992), so it is interesting to look at how things have changed since then. Figure 5.1 gives a breakdown of the number of lines of code in GHC divided up into the major components, comparing the current tallies with those from 1992. Module Lines (1992) Lines (2011) Increase Compiler Main 997 11,150 11.2 Parser 1,055 4,098 3.9 Renamer 2,828 4,630 1.6 Type checking 3,352 24,097 7.2 Desugaring 1,381 7,091 5.1 Core transformations 1,631 9,480 5.8 STG transformations 814 840 1 Data-Parallel Haskell — 3,718 — Code generation 2913 11,003 3.8 Native code generation — 14,138 — LLVM code generation — 2,266 — GHCi — 7,474 — Haskell abstract syntax 2,546 3,700 1.5 Core language 1,075 4,798 4.5 STG language 517 693 1.3 C-- (was Abstract C) 1,416 7,591 5.4 Identifier representations 1,831 3,120 1.7 Type representations 1,628 3,808 2.3 Prelude definitions 3,111 2,692 0.9 Utilities 1,989 7,878 3.96 Profiling 191 367 1.92 Compiler Total 28,275 139,955 4.9

Runtime System All C and C-- code 43,865 48,450 1.10 Figure 5.1: Lines of code in GHC, past and present There are some notable aspects of these figures: Despite nearly 20 years of non-stop development the compiler has only increased in size by a factor of 5, from around 28,000 to around 140,000 lines of Haskell code. We obsessively refactor while adding new code, keeping the code base as fresh as possible.

There are several new components, although these only account for about 28,000 new lines. Much of the new components are concerned with code generation: native code generators for various processors, and an LLVM code generator. (Formerly the "Low Level Virtual Machine", the LLVM project includes a generic code-generator with targets for many different processors. For more information see http://llvm.org/, and the chapter on LLVM in Volume 1 of The Architecture of Open Source Applications.) The infrastructure for the interactive interpreter GHCi also added over 7,000 lines.

The biggest increase in a single component is the type checker, where over 20,000 lines were added. This is unsurprising given that much of the recent research using GHC has been into new type system extensions (for example GADTs [PVWW06] and Type Families [CKP05]).

A lot of code has been added to the Main component; this is partly because there was previously a 3,000-line Perl script called the "driver" that was rewritten in Haskell and moved into GHC proper, and also because support for compiling multiple modules was added.

component; this is partly because there was previously a 3,000-line Perl script called the "driver" that was rewritten in Haskell and moved into GHC proper, and also because support for compiling multiple modules was added. The runtime system has barely grown: it is only 10% larger, despite having accumulated a lot of new functionality and being ported to more platforms. We rewrote it completely around 1997.

GHC has a complex build system, which today comprises about 6,000 lines of GNU make code. It is on its fourth complete rewrite, the latest being about two years ago, and each successive iteration has reduced the amount of code. The Compiler We can divide the compiler into three: The compilation manager, which is responsible for the compilation of multiple Haskell source files. The job of the compilation manager is to figure out in which order to compile the different files, and to decide which modules do not need to be recompiled because none of their dependencies have changed since the last time they were compiled.

The Haskell compiler (we abbreviate this as Hsc inside GHC), which handles the compilation of a single Haskell source file. As you might imagine, most of the action happens in here. The output of Hsc depends on what backend is selected: assembly, LLVM code, or bytecode.

inside GHC), which handles the compilation of a single Haskell source file. As you might imagine, most of the action happens in here. The output of depends on what backend is selected: assembly, LLVM code, or bytecode. The pipeline, which is responsible for composing together any necessary external programs with Hsc to compile a Haskell source file to object code. For example, a Haskell source file may need preprocessing with the C preprocessor before feeding to Hsc , and the output of Hsc is usually an assembly file that must be fed into the assembler to create an object file. The compiler is not simply an executable that performs these functions; it is itself a library with a large API that can be used to build other tools that work with Haskell source code, such as IDEs and analysis tools. Compiling Haskell Code As with most compilers, compiling a Haskell source file proceeds in a sequence of phases, with the output of each phase becoming the input of the subsequent phase. The overall structure of the different phases is illustrated in Figure 5.2. Figure 5.2: The compiler phases Parsing We start in the traditional way with parsing, which takes as input a Haskell source file and produces as output abstract syntax. In GHC the abstract syntax datatype HsSyn is parameterised by the types of the identifiers it contains, so an abstract syntax tree has type HsSyn t for some type of identifiers t . This enables us to add more information to identifiers as the program passes through the various stages of the compiler, while reusing the same type of abstract syntax trees. The output of the parser is an abstract syntax tree in which the identifiers are simple strings, which we call RdrName . Hence, the abstract syntax produced by the parser has type HsSyn RdrName . GHC uses the tools Alex and Happy to generate its lexical analysis and parsing code respectively, which are analogous to the tools lex and yacc for C. GHC's parser is purely functional. In fact, the API of the GHC library provides a pure function called parser that takes a String (and a few other things) and returns either the parsed abstract syntax or an error message. Renaming Renaming is the process of resolving all of the identifiers in the Haskell source code into fully qualified names, at the same time identifying any out-of-scope identifiers and flagging errors appropriately. In Haskell it is possible for a module to re-export an identifier that it imported from another module. For example, suppose module A defines a function called f , and module B imports module A and re-exports f . Now, if a module C imports module B , it can refer to f by the name B.f —even though f is originally defined in module A . This is a useful form of namespace manipulation; it means that a library can use whatever module structure it likes internally, but expose a nice clean API via a few interface modules that re-export identifiers from the internal modules. The compiler however has to resolve all this, so that it knows what each name in the source code corresponds to. We make a clean distinction between the entities, the "things themselves" (in our example, A.f ), and the names by which the entities can be referred to (e.g., B.f ). At any given point in the source code, there are a set of entities in scope, and each may be known by one or more different names. The job of the renamer is to replace each of the names in the compiler's internal representation of the code by a reference to a particular entity. Sometimes a name can refer to several different entities; by itself that is not an error, but if the name is actually used, then the renamer will flag an ambiguity error and reject the program. Renaming takes Haskell abstract syntax ( HsSyn ,RdrName ) as input, and also produces abstract syntax as output ( HsSyn Name ). Here a Name is a reference to a particular entity. Resolving names is the main job of the renamer, but it performs a plethora of other tasks too: collecting the equations of a function together and flagging an error if they have differing numbers of arguments; rearranging infix expressions according to the fixity of the operators; spotting duplicate declarations; generating warnings for unused identifiers, and so on. Type Checking Type checking, as one might imagine, is the process of checking that the Haskell program is type-correct. If the program passes the type checker, then it is guaranteed to not crash at runtime. (The term "crash" here has a formal definition that includes hard crashes like "segmentation fault", but not things like pattern-matching failure. The non-crash guarantee can be subverted by using certain unsafe language features, such as the Foreign Function Interface.) The input to the type checker is HsSyn Name (Haskell source with qualified names), and the output is HsSyn Id . An Id is a Name with extra information: notably a type. In fact, the Haskell syntax produced by the type checker is fully decorated with type information: every identifier has its type attached, and there is enough information to reconstruct the type of any subexpression (which might be useful for an IDE, for example). In practice, type checking and renaming may be interleaved, because the Template Haskell feature generates code at runtime that itself needs to be renamed and type checked. Desugaring, and the Core language Haskell is a rather large language, containing many different syntactic forms. It is intended to be easy for humans to read and write—there is a wide range of syntactic constructs which gives the programmer plenty of flexibility in choosing the most appropriate construct for the situation at hand. However, this flexibility means that there are often several ways to write the same code; for example, an if expression is identical in meaning to a case expression with True and False branches, and list-comprehension notation can be translated into calls to map , filter , and concat . In fact, the definition of the Haskell language defines all these constructs by their translation into simpler constructs; the constructs that can be translated away like this are called "syntactic sugar". It is much simpler for the compiler if all the syntactic sugar is removed, because the subsequent optimisation passes that need to work with the Haskell program have a smaller language to deal with. The process of desugaring therefore removes all the syntactic sugar, translating the full Haskell syntax into a much smaller language that we call Core . We'll talk about Core in detail later. Optimisation Now that the program is in Core , the process of optimisation begins. One of GHC's great strengths is in optimising away layers of abstraction, and all of this work happens at the Core level. Core is a tiny functional language, but it is a tremendously flexible medium for expressing optimisations, ranging from the very high-level, such as strictness analysis, to the very low-level, such as strength reduction. Each of the optimisation passes takes Core and produces Core . The main pass here is called the Simplifier, whose job it is to perform a large collection of correctness-preserving transformations, with the goal of producing a more efficient program. Some of these transformations are simple and obvious, such as eliminating dead code or reducing a case expression when the value being scrutinised is known, and some are more involved, such as function inlining and applying rewrite rules (discussed later). The simplifier is normally run between the other optimisation passes, of which there are about six; which passes are actually run and in which order depends on the optimisation level selected by the user. Code Generation Once the Core program has been optimised, the process of code generation begins. After a couple of administrative passes, the code takes one of two routes: either it is turned into byte code for execution by the interactive interpreter, or it is passed to the code generator for eventual translation to machine code. The code generator first converts the Core into a language called STG , which is essentially just Core annotated with more information required by the code generator. Then, STG is translated to Cmm , a low-level imperative language with an explicit stack. From here, the code takes one of three routes: Native code generation : GHC contains simple native code generators for a few processor architectures. This route is fast, and generates reasonable code in most cases.

: GHC contains simple native code generators for a few processor architectures. This route is fast, and generates reasonable code in most cases. LLVM code generation : The Cmm is converted to LLVM code and passed to the LLVM compiler. This route can produce significantly better code in some cases, although it takes longer than the native code generator.

: The is converted to LLVM code and passed to the LLVM compiler. This route can produce significantly better code in some cases, although it takes longer than the native code generator. C code generation: GHC can produce ordinary C code. This route produces significantly slower code than the other two routes, but can be useful for porting GHC to new platforms.

5.3. Key Design Choices In this section we focus on a handful of the design choices that have been particularly effective in GHC. The Intermediate Language Expressions t,e,u ::= x Variables | K Data constructors | k Literals | λ x:σ.e | e u Value abstraction and application | Λ a:η.e | e φ Type abstraction and application | let x:τ = e in u Local bindings | case e of p→u Case expressions | e ▷ γ Casts | ⌊ γ ⌋ Coercions p ::= K c:η x:τ Patterns Figure 5.3: The syntax of Core A typical structure for a compiler for a statically-typed language is this: the program is type checked, and transformed to some untyped intermediate language, before being optimised. GHC is different: it has a statically-typed intermediate language. As it turns out, this design choice has had a pervasive effect on the design and development of GHC. GHC's intermediate language is called Core (when thinking of the implementation) or System FC (when thinking about the theory). Its syntax is given in Figure 5.3. The exact details are not important here; the interested reader can consult [SCPD07] for more details. For our present purposes, however, the following points are the key ones: Haskell is a very large source language. The data type representing its syntax tree has literally hundreds of constructors. In contrast Core is a tiny, principled, lambda calculus. It has extremely few syntactic forms, yet we can translate all of Haskell into Core .

Haskell is an implicitly-typed source language. A program may have few or no type annotations; instead it is up to the type inference algorithm to figure out the type of every binder and sub-expressions. This type inference algorithm is complex, and occasionally somewhat ad hoc, reflecting the design compromises that every real programming language embodies. In contrast Core is an explicitly-typed language. Every binder has an explicit type, and terms include explicit type abstractions and applications. Core enjoys a very simple, fast type checking algorithm that checks that the program is type correct. The algorithm is entirely straightforward; there are no ad hoc compromises. All of GHC's analysis and optimisation passes work on Core . This is great: because Core is such a tiny language an optimisation has only a few cases to deal with. Although Core is small, it is extremely expressive—System F was, after all, originally developed as a foundational calculus for typed computation. When new language features are added to the source language (and that happens all the time) the changes are usually restricted to the front end; Core stays unchanged, and hence so does most of the compiler. But why is Core typed? After all, if the type inference engine accepts the source program, that program is presumably well typed, and each optimisation pass presumably maintains that type-correctness. Core may enjoy a fast type checking algorithm, but why would you ever want to run it? Moreover, making Core typed carries significant costs, because every transformation or optimisation pass must produce a well-typed program, and generating all those type annotations is often non-trivial. Nevertheless, it has been a huge win to have an explicitly-typed intermediate language, for several reasons: Running the Core type checker (we call it Lint ) is a very powerful consistency check on the compiler itself. Imagine that you write an "optimisation" that accidentally generates code that treats an integer value as a function, and tries to call it. The chances are that the program will segmentation fault, or fail at runtime in a bizarre way. Tracing a seg-fault back to the particular optimisation pass that broke the program is a long road. Now imagine instead that we run Lint after every optimisation pass (and we do, if you use the flag -dcore-lint ): it will report a precisely located error immediately after the offending optimisation. What a blessing. Of course, type soundness is not the same as correctness: Lint will not signal an error if you "optimise" (x*1) to 1 instead of to x. But if the program passes Lint , it will guarantee to run without seg-faults; and moreover in practice we have found that it is surprisingly hard to accidentally write optimisations that are type-correct but not semantically correct.

type checker (we call it ) is a very powerful consistency check on the compiler itself. Imagine that you write an "optimisation" that accidentally generates code that treats an integer value as a function, and tries to call it. The chances are that the program will segmentation fault, or fail at runtime in a bizarre way. Tracing a seg-fault back to the particular optimisation pass that broke the program is a long road. The type inference algorithm for Haskell is very large and very complex: a glance at Figure 5.1 confirms that the type checker is by far the largest single component of GHC. Large and complex means error-prone. But Lint serves as an 100% independent check on the type inference engine; if the type inference engine accepts a program that is not, in fact, type-correct, Lint will reject it. So Lint serves as a powerful auditor of the type inference engine.

serves as an 100% independent check on the type inference engine; if the type inference engine accepts a program that is not, in fact, type-correct, will reject it. So serves as a powerful auditor of the type inference engine. The existence of Core has also proved to be a tremendous sanity check on the design of the source language. Our users constantly suggest new features that they would like in the language. Sometimes these features are manifestly "syntactic sugar", convenient new syntax for something you can do already. But sometimes they are deeper, and it can be hard to tell how far-reaching the feature is. Core gives us a precise way to evaluate such features. If the feature can readily be translated into Core , that reassures us that nothing fundamentally new is going on: the new feature is syntactic-sugar-like. On the other hand, if it would require an extension to Core , then we think much, much more carefully. Nevertheless, it has been a huge win to have an explicitly-typed intermediate language, for several reasons: In practice Core has been incredibly stable: over a 20-year time period we have added exactly one new major feature to Core (namely coercions and their associated casts). Over the same period, the source language has evolved enormously. We attribute this stability not to our own brilliance, but rather to the fact that Core is based directly on foundational mathematics: bravo Girard! Type Checking the Source Language One interesting design decision is whether type checking should be done before or after desugaring. The trade-offs are these: Type checking before desugaring means that the type checker must deal directly with Haskell's very large syntax, so the type checker has many cases to consider. If we desugared into (an untyped variant of) Core first, one might hope that the type checker would become much smaller.

first, one might hope that the type checker would become much smaller. On the other hand, type checking after desugaring would impose a significant new obligation: that desugaring does not affect which programs are type-correct. After all, desugaring implies a deliberate loss of information. It is probably the case that in 95% of the cases there is no problem, but any problem here would force some compromise in the design of Core to preserve some extra information.

to preserve some extra information. Most seriously of all, type checking a desugared program would make it much harder to report errors that relate to the original program text, and not to its (sometimes elaborate) desugared version. Most compilers type check after desugaring, but for GHC we made the opposite choice: we type check the full original Haskell syntax, and then desugar the result. It sounds as if adding a new syntactic construct might be complicated, but (following the French school) we have structured the type inference engine in a way that makes it easy. Type inference is split into two parts: Constraint generation: walk over the source syntax tree, generating a collection of type constraints. This step deals with the full syntax of Haskell, but it is very straightforward code, and it is easy to add new cases. Constraint solving: solve the gathered constraints. This is where the subtlety of the type inference engine lies, but it is independent of the source language syntax, and would be the same for a much smaller or much larger language. On the whole, the type-check-before-desugar design choice has turned out to be a big win. Yes, it adds lines of code to the type checker, but they are simple lines. It avoids giving two conflicting roles to the same data type, and makes the type inference engine less complex, and easier to modify. Moreover, GHC's type error messages are pretty good. No Symbol Table Compilers usually have one or more data structures known as symbol tables, which are mappings from symbols (e.g., variables) to some information about the variable, such as its type, or where in the source code it was defined. In GHC we use symbol tables quite sparingly; mainly in the renamer and type checker. As far as possible, we use an alternative strategy: a variable is a data structure that contains all the information about itself. Indeed, a large amount of information is reachable by traversing the data structure of a variable: from a variable we can see its type, which contains type constructors, which contain their data constructors, which themselves contain types, and so on. For example, here are some data types from GHC (heavily abbreviated and simplified): data Id = MkId Name Type data Type = TyConApp TyCon [Type] | .... data TyCon = AlgTyCon Name [DataCon] | ... data DataCon = MkDataCon Name Type ... An Id contains its Type . A Type might be an application of a type constructor to some arguments (e.g., Maybe Int ), in which case it contains the TyCon . A TyCon can be an algebraic data type, in which case it includes a list of its data constructors. Each DataCon includes its Type , which of course mentions the TyCon . And so on. The whole structure is highly interconnected. Indeed it is cyclic; for example, a TyCon may contain a DataCon which contains a Type , which contains the very TyCon we started with. This approach has some advantages and disadvantages: Many queries that would require a lookup in a symbol table are reduced to a simple field access, which is great for efficiency and code clarity.

There is no need to carry around extra symbol tables, the abstract syntax tree already contains all the information.

The space overheads are better: all instances of the same variable share the same data structure, and there is no space needed for the table.

The only difficulties arise when we need to change any of the information associated with a variable. This is where a symbol table has the advantage: we would just change the entry in the symbol table. In GHC we have to traverse the abstract syntax tree and replace all the instances of the old variable with the new one; indeed the simplifier does this regularly, as it needs to update certain optimisation-related information about each variable. It is hard to know whether it would be better or worse overall to use symbol tables, because this aspect of the design is so fundamental that it is almost impossible to change. Still, avoiding symbol tables is a natural choice in the purely functional setting, so it seems likely that this approach is a good choice for Haskell. Inter-Module Optimisation Functional languages encourage the programmer to write small definitions. For example, here is the definition of && from the standard library: (&&) :: Bool -> Bool -> Bool True && True = True _ && _ = False If every use of such a function really required a function call, efficiency would be terrible. One solution is to make the compiler treat certain functions specially; another is to use a pre-processor to replace a "call" with the desired inline code. All of these solutions are unsatisfactory in one way or another, especially as another solution is so obvious: simply inline the function. To "inline a function" means to replace the call by a copy of the function body, suitably instantiating its parameters. In GHC we have systematically adopted this approach [PM02]. Virtually nothing is built into the compiler. Instead, we define as much as possible in libraries, and use aggressive inlining to eliminate the overheads. This means that programmers can define their own libraries that will be inlined and optimised as well as the ones that come with GHC. A consequence is that GHC must be able to do cross-module, and indeed cross-package, inlining. The idea is simple: When compiling a Haskell module Lib.hs , GHC produces object code in Lib.o and an "interface file" in Lib.hi . This interface file contains information about all the functions that Lib exports, including both their types and, for sufficiently small functions, their definitions.

, GHC produces object code in and an "interface file" in . This interface file contains information about all the functions that exports, including both their types and, for sufficiently small functions, their definitions. When compiling a module Client.hs that imports Lib , GHC reads the interface Lib.hi . So if Client calls a function Lib.f defined in Lib , GHC can use the information in Lib.hi to inline Lib.f . By default GHC will expose the definition of a function in the interface file only if the function is "small" (there are flags to control this size threshold). But we also support an INLINE pragma, to instruct GHC to inline the definition aggressively at call sites, regardless of size, thus: foo :: Int -> Int {-# INLINE foo #-} foo x = <some big expression> Cross-module inlining is absolutely essential for defining super-efficient libraries, but it does come with a cost. If the author upgrades his library, it is not enough to re-link Client.o with the new Lib.o , because Client.o contains inlined fragments of the old Lib.hs , and they may well not be compatible with the new one. Another way to say this is that the ABI (Application Binary Interface) of Lib.o has changed in a way that requires recompilation of its clients. In fact, the only way for compilation to generate code with a fixed, predictable ABI is to disable cross-module optimisation, and this is typically too high a price to pay for ABI compatibility. Users working with GHC will usually have the source code to their entire stack available, so recompiling is not normally an issue (and, as we will describe later, the package system is designed around this mode of working). However, there are situations where recompiling is not practical: distributing bug fixes to libraries in a binary OS distribution, for example. In the future we hope it may be possible to find a compromise solution that allows retaining ABI compatibility while still allowing some cross-module optimisation to take place.

5.4. Extensibility It is often the case that a project lives or dies according to how extensible it is. A monolithic piece of software that is not extensible has to do everything and do it right, whereas an extensible piece of software can be a useful base even if it doesn't provide all the required functionality out of the box. Open source projects are of course extensible by definition, in that anyone can take the code and add their own features. But modifying the original source code of a project maintained by someone else is not only a high-overhead approach, it is also not conducive to sharing your extension with others. Therefore successful projects tend to offer forms of extensibility that do not involve modifying the core code, and GHC is no exception in this respect. User-Defined Rewrite Rules The core of GHC is a long sequence of optimisation passes, each of which performs some semantics-preserving transformation, Core into Core . But the author of a library defines functions that often have some non-trivial, domain-specific transformations of their own, ones that cannot possibly be predicted by GHC. So GHC allows library authors to define rewrite rules that are used to rewrite the program during optimisation [PTH01]. In this way, programmers can, in effect, extend GHC with domain-specific optimisations. One example is the foldr/build rule, which is expressed like this: {-# RULES "fold/build" forall k z (g::forall b. (a->b->b) -> b -> b) . foldr k z (build g) = g k z #-} The entire rule is a pragma, introduced by {-# RULES . The rule says that whenever GHC sees the expression (foldr k z (build g)) it should rewrite it to (g k z) . This transformation is semantics-preserving, but it takes a research paper to argue that it is [GLP93], so there is no chance of GHC performing it automatically. Together with a handful of other rules, and some INLINE pragmas, GHC is able to fuse together list-transforming functions. For example, the two loops in (map f (map g xs)) are fused into one. Although rewrite rules are simple and easy to use, they have proved to be a very powerful extension mechanism. When we first introduced the feature into GHC ten years ago we expected it to be an occasionally useful facility. But in practice it has turned out to be useful in very many libraries, whose efficiency often depends crucially on rewrite rules. For example, GHC's own base library contains upward of 100 rules, while the popular vector library uses several dozen. Compiler Plugins One way in which a compiler can offer extensibility is to allow programmers to write a pass that is inserted directly into the compiler's pipeline. Such passes are often called "plugins". GHC supports plugins in the following way: The programmer writes a Core to Core pass, as an ordinary Haskell function in a module P.hs , say, and compiles it to object code.

to pass, as an ordinary Haskell function in a module , say, and compiles it to object code. When compiling some module, the programmer uses the command-line flag -plugin P . (Alternatively, he can give the flag in a pragma at the start of the module.)

. (Alternatively, he can give the flag in a pragma at the start of the module.) GHC searches for P.o , dynamically links it into the running GHC binary, and calls it at the appropriate point in the pipeline. But what is "the appropriate point in the pipeline"? GHC does not know, and so it allows the plugin to make that decision. As a result of this and other matters, the API that the plugin must offer is a bit more complicated than a single Core to Core function—but not much. Plugins sometimes require, or produce, auxiliary plugin-specific data. For example, a plugin might perform some analysis on the functions in the module being compiled ( M.hs , say), and might want to put that information in the interface file M.hi , so that the plugin has access to that information when compiling modules that import M . GHC offers an annotation mechanism to support this. Plugins and annotations are relatively new to GHC. They have a higher barrier to entry than rewrite rules, because the plugin is manipulating GHC's internal data structures, but of course they can do much more. It remains to be seen how widely they will be used. GHC as a Library: The GHC API One of GHC's original goals was to be a modular foundation that others could build on. We wanted the code of GHC to be as transparent and well-documented as possible, so that it could be used as the basis for research projects by others; we imagined that people would want to make their own modifications to GHC to add new experimental features or optimisations. Indeed, there have been some examples of this: for example, there exists a version of GHC with a Lisp front-end, and a version of GHC that generates Java code, both developed entirely separately by individuals with little or no contact with the GHC team. However, producing modified versions of GHC represents only a small subset of the ways in which the code of GHC can be re-used. As the popularity of the Haskell language has grown, there has been an increasing need for tools and infrastructure that understand Haskell source code, and GHC of course contains a lot of the functionality necessary for building these tools: a Haskell parser, abstract syntax, type checker and so on. With this in mind, we made a simple change to GHC: rather than building GHC as a monolithic program, we build GHC as a library, that is then linked with a small Main module to make the GHC executable itself, but also shipped in library form so that users can call it from their own programs. At the same time we built an API to expose GHC's functionality to clients. The API provides enough functionality to implement the GHC batch compiler and the GHCi interactive environment, but it also provides access to individual passes such as the parser and type checker, and allows the data structures produced by these passes to be inspected. This change has given rise to a wide range of tools built using the GHC API, including: A documentation tool, Haddock, which reads Haskell source code and produces HTML documentation.

New versions of the GHCi front end with additional features; e.g., ghci-haskeline which was subsequently merged back into GHC.

IDEs that offer advanced navigation of Haskell source code; e.g., Leksah.

hint, a simpler API for on-the-fly evaluation of Haskell source code. The Package System The package system has been a key factor in the growth in use of the Haskell language in recent years. Its main purpose is to enable Haskell programmers to share code with each other, and as such it is an important aspect of extensibility: the package system extends the shared codebase beyond GHC itself. The package system embodies various pieces of infrastructure that together make sharing code easy. With the package system as the enabler, the community has built a large body of shared code; rather than relying on libraries from a single source, Haskell programmers draw on libraries developed by the whole community. This model has worked well for other languages; CPAN for Perl, for example, although Haskell being a predominantly compiled rather than interpreted language presents a somewhat different set of challenges. Basically, the package system lets a user manage libraries of Haskell code written by other people, and use them in their own programs and libraries. Installing a Haskell library is as simple as uttering a single command, for example: $ cabal install zlib downloads the code for the zlib package from http://hackage.haskell.org, compiles it using GHC, installs the compiled code somewhere on your system (e.g., in your home directory on a Unix system), and registers the installation with GHC. Furthermore, if zlib depends on any other packages that are not yet installed, those will also be downloaded, compiled and installed automatically before zlib itself is compiled. It is a tremendously smooth way to work with libraries of Haskell code shared by others. The package system is made of four components, only the first of which is strictly part of the GHC project: Tools for managing the package database, which is simply a repository for information about the packages installed on your system. GHC reads the package database when it starts up, so that it knows which packages are available and where to find them.

A library called Cabal (Common Architecture for Building Applications and Libraries), which implements functionality for building, installing and registering individual packages.

(Common Architecture for Building Applications and Libraries), which implements functionality for building, installing and registering individual packages. A website at http://hackage.haskell.org which hosts packages written and uploaded by users. The website automatically builds documentation for the packages which can be browsed online. At the time of writing, Hackage is hosting over 3,000 packages covering functionality including database libraries, web frameworks, GUI toolkits, data structures, and networking.

The cabal tool which ties together the Hackage website and the Cabal library: it downloads packages from Hackage, resolves dependencies, and builds and installs packages in the right order. New packages can also be uploaded to Hackage using cabal from the command line. These components have been developed over several years by members of the Haskell community and the GHC team, and together they make a system that fits perfectly with the open source development model. There are no barriers to sharing code or using code that others have shared (provided you respect the relevant licenses, of course). You can be using a package that someone else has written literally within seconds of finding it on Hackage. Hackage has been so successful that the remaining problems it has are now those of scale: users find it difficult to choose amongst the four different database frameworks, for example. Ongoing developments are aimed at solving these problems in ways that leverage the community. For example, allowing users to comment and vote on packages will make it easier to find the best and most popular packages, and collecting data on build success or failures from users and reporting the results will help users avoid packages that are unmaintained or have problems.

5.5. The Runtime System The Runtime System is a library of mostly C code that is linked into every Haskell program. It provides the support infrastructure needed for running the compiled Haskell code, including the following main components: Memory management, including a parallel, generational, garbage collector;

Thread management and scheduling;

The primitive operations provided by GHC;

A bytecode interpreter and dynamic linker for GHCi. The rest of this section is divided into two: first we focus on a couple of the aspects of the design of the RTS that we consider to have been successful and instrumental in making it work so well, and secondly we talk about the coding practices and infrastructure we have built in the RTS for coping with what is a rather hostile programming environment. Key Design Decisions In this section we describe two of the design decisions in the RTS that we consider to have been particularly successful. The Block Layer The garbage collector is built on top of a block layer that manages memory in units of blocks, where a block is a multiple of 4 KB in size. The block layer has a very simple API: typedef struct bdescr_ { void * start; struct bdescr_ * link; struct generation_ * gen; // generation // .. various other fields } bdescr; bdescr * allocGroup (int n); void freeGroup (bdescr *p); bdescr * Bdescr (void *p); // a macro This is the only API used by the garbage collector for allocating and deallocating memory. Blocks of memory are allocated with allocGroup and freed with freeGroup . Every block has a small structure associated with it called a block descriptor ( bdescr ). The operation Bdescr(p) returns the block descriptor associated with an arbitrary address p ; this is purely an address calculation based on the value of p and compiles to a handful of arithmetic and bit-manipulation instructions. Blocks may be linked together into chains using the link field of the bdescr , and this is the real power of the technique. The garbage collector needs to manage several distinct areas of memory such as generations, and each of these areas may need to grow or shrink over time. By representing memory areas as linked lists of blocks, the GC is freed from the difficulties of fitting multiple resizable memory areas into a flat address space. The implementation of the block layer uses techniques that are well-known from C's malloc()/free() API; it maintains lists of free blocks of various sizes, and coalesces free areas. The operations freeGroup() and allocGroup() are carefully designed to be O(1). One major advantage of this design is that it needs very little support from the OS, and hence is great for portability. The block layer needs to allocate memory in units of 1 MB, aligned to a 1 MB boundary. While none of the common OSs provide this functionality directly, it is implementable without much difficulty in terms of the facilities they do provide. The payoff is that GHC has no dependence on the particular details of the address-space layout used by the OS, and it coexists peacefully with other users of the address space, such as shared libraries and operating system threads. There is a small up-front complexity cost for the block layer, in terms of managing chains of blocks rather than contiguous memory. However, we have found that this cost is more than repaid in flexibility and portability; for example, the block layer enabled a particularly simple algorithm for parallel GC to be implemented [MHJP08]. Lightweight Threads and Parallelism We consider concurrency to be a vitally important programming abstraction, particularly for building applications like web servers that need to interact with large numbers of external agents simultaneously. If concurrency is an important abstraction, then it should not be so expensive that programmers are forced to avoid it, or build elaborate infrastructure to amortise its cost (e.g., thread pools). We believe that concurrency should just work, and be cheap enough that you don't worry about forking threads for small tasks. All operating systems provide threads that work perfectly well, the problem is that they are far too expensive. Typical OSs struggle to handle thousands of threads, whereas we want to manage threads by the million. Green threads, otherwise known as lightweight threads or user-space threads, are a well-known technique for avoiding the overhead of operating system threads. The idea is that threads are managed by the program itself, or a library (in our case, the RTS), rather than by the operating system. Managing threads in user space should be cheaper, because fewer traps into the operating system are required. In the GHC RTS we take full advantage of this idea. A context switch only occurs when the thread is at a safe point, where very little additional state needs to be saved. Because we use accurate GC, the stack of the thread can be moved and expanded or shrunk on demand. Contrast these with OS threads, where every context switch must save the entire processor state, and where stacks are immovable so a large chunk of address space has to be reserved up front for each thread. Green threads can be vastly more efficient than OS threads, so why would anyone want to use OS threads? It comes down to three main problems: Blocking and foreign calls. A thread should be able to make a call to an OS API or a foreign library that blocks, without blocking all the other threads in the system.

Parallelism. Threads should automatically run in parallel if there are multiple processor cores on the system.

Some external libraries (notably OpenGL and some GUI libraries) have APIs that must be called from the same OS thread each time, because they use thread-local state. It turns out that all of these are difficult to arrange with green threads. Nevertheless, we persevered with green threads in GHC and found solutions to all three: When a Haskell thread makes a foreign call, another OS thread takes over the execution of the remaining Haskell threads [MPT04]. A small pool of OS threads are maintained for this purpose, and new ones are created on demand.

GHC's scheduler multiplexes many lightweight Haskell threads onto a few heavyweight OS threads; it implements a transparent M:N threading model. Typically N is chosen to be the same as the number of processor cores in the machine, allowing real parallelism to take place but without the overhead of having a full OS thread for each lightweight Haskell thread. In order to run Haskell code, an OS thread must hold a Capability: a data structure that holds the resources required to execute Haskell code, such as the nursery (memory where new objects are created). Only one OS thread may hold a given Capability at a time. (We have also called it a "Haskell Execution Context", but the code currently uses the Capability terminology.)

We provide an API for creating a bound thread: a Haskell thread that is tied to one specific OS thread, such that any foreign calls made by this Haskell thread are guaranteed to be made by that OS thread. So in the vast majority of cases, Haskell's threads behave exactly like OS threads: they can make blocking OS calls without affecting other threads, and they run in parallel on a multicore machine. But they are orders of magnitude more efficient, in terms of both time and space. Having said that, the implementation does have one problem that users occasionally run into, especially when running benchmarks. We mentioned above that lightweight threads derive some of their efficiency by only context-switching at "safe points", points in the code that the compiler designates as safe, where the internal state of the virtual machine (stack, heap, registers, etc.) is in a tidy state and garbage collection could take place. In GHC, a safe point is whenever memory is allocated, which in almost all Haskell programs happens regularly enough that the program never executes more than a few tens of instructions without hitting a safe point. However, it is possible in highly optimised code to find loops that run for many iterations without allocating memory. This tends to happen often in benchmarks (e.g., functions like factorial and Fibonacci). It occurs less often in real code, although it does happen. The lack of safe points prevents the scheduler from running, which can have detrimental effects. It is possible to solve this problem, but not without impacting the performance of these loops, and often people care about saving every cycle in their inner loops. This may just be a compromise we have to live with.

5.6. Developing GHC GHC is a single project with a twenty-year life span, and is still in a ferment of innovation and development. For the most part our infrastructure and tooling has been conventional. For example, we use a bug tracker (Trac), a wiki (also Trac), and Git for revision control. (This revision-control mechanism evolved from purely manual, then CVS, then Darcs, before finally moving to Git in 2010.) There are a few points that may be less universal, and we offer them here. Comments and Notes One of the most serious difficulties in a large, long-lived project is keeping technical documentation up to date. We have no silver bullet, but we offer one low-tech mechanism that has served us particularly well: Notes . When writing code, there is often a moment when a careful programmer will mentally say something like "This data type has an important invariant". She is faced with two choices, both unsatisfactory. She can add the invariant as a comment, but that can make the data type declaration too long, so that it is hard to see what the constructors are. Alternatively, she can document the invariant elsewhere, and risk it going out of date. Over twenty years, everything goes out of date! Thus motivated, we developed the following very simple convention: Comments of any significant size are not interleaved with code, but instead set off by themselves, with a heading in standard form, thus: Note [Equality-constrained types] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The type forall ab. (a ~ [b]) => blah is encoded like this: ForAllTy (a:*) $ ForAllTy (b:*) $ FunTy (TyConApp (~) [a, [b]]) $ blah

A the point where the comment is relevant, we add a short comment referring to the Note: data Type = FunTy Type Type -- See Note [Equality-constrained types] | ... The comment highlights that something interesting is going on, and gives a precise reference to the comment that explains. It sounds trivial, but the precision is vastly better than our previous habit of saying "see the comment above", because it often was not clear which of the many comments above was intended, and after a few years the comment was not even above (it was below, or gone altogether). Not only is it possible to go from the code that refers to the Note to the Note itself, but the reverse is also possible, and that is often useful. Moreover, the same Note may be referred to from multiple points in the code. This simple, ASCII-only technique, with no automated support, has transformed our lives: GHC has around 800 Notes , and the number grows daily. How to Keep On Refactoring The code of GHC is churning just as quickly as it was ten years ago, if not more so. There is no doubt that the complexity of the system has increased manyfold over that same time period; we saw measures of the amount of code in GHC earlier. Yet, the system remains manageable. We attribute this to three main factors: There's no substitute for good software engineering. Modularity always pays off: making the APIs between components as small as possible makes the individual components more flexible because they have fewer interdependencies. For example, GHC's Core{} datatype being small reduces the coupling between Core-to-Core passes, to the extent that they are almost completely independent and can be run in arbitrary order.

datatype being small reduces the coupling between Core-to-Core passes, to the extent that they are almost completely independent and can be run in arbitrary order. Developing in a strongly-typed language makes refactoring a breeze. Whenever we need to change a data type, or change the number of arguments or type of a function, the compiler immediately tells us what other places in the code need to be fixed. Simply having an absolute guarantee that a large class of errors have been statically ruled out saves a huge amount of time, especially when refactoring. It is scary to imagine how many hand-written test cases we would need to provide the same level of coverage that the type system provides.

When programming in a purely functional language, it is hard to introduce accidental dependencies via state. If you decide that you suddenly need access to a piece of state deep in an algorithm, in an imperative language you might be tempted to just make the state globally visible rather than explicitly pass it down to the place that needs it. This way eventually leads to a tangle of invisible dependencies, and brittle code: code that breaks easily when modified. Pure functional programming forces you to make all the dependencies explicit, which exerts some negative pressure on adding new dependencies, and fewer dependencies means greater modularity. Certainly when it is necessary to add a new dependency then purity makes you write more code to express the dependency, but in our view it is a worthwhile price to pay for the long-term health of the code base. As an added benefit, purely functional code is thread-safe by construction and tends to be easier to parallelise. Crime Doesn't Pay Looking back over the changes we've had to make to GHC as it has grown, a common lesson emerges: being less than purely functional, whether for the purposes of efficiency or convenience, tends to have negative consequences down the road. We have a couple of great examples of this: GHC uses a few data structures that rely on mutation internally. One is the FastString type, which uses a single global hash table; another is a global NameCache that ensures all external names are assigned a unique number. When we tried to parallelise GHC (that is, make GHC compile multiple modules in parallel on a multicore processor), these data structures based on mutation were the only sticking points. Had we not resorted to mutation in these places, GHC would have been almost trivial to parallelise. In fact, although we did build a prototype parallel version of GHC, GHC does not currently contain support for parallel compilation, but that is largely because we have not yet invested the effort required to make these mutable data structures thread-safe.

type, which uses a single global hash table; another is a global that ensures all external names are assigned a unique number. When we tried to parallelise GHC (that is, make GHC compile multiple modules in parallel on a multicore processor), these data structures based on mutation were the only sticking points. Had we not resorted to mutation in these places, GHC would have been almost trivial to parallelise. GHC's behaviour is governed to a large extent by command-line flags. These command-line flags are by definition constant over a given run of GHC, so in early versions of GHC we made the values of these flags available as top-level constants. For example, there was a top-level value opt_GlasgowExts of type Bool , that governed whether certain language extensions should be enabled or not. Top-level constants are highly convenient, because their values don't have to be explicitly passed as arguments to all the code that needs access to them. Of course these options are not really constants, because they change from run to run, and the definition of opt_GlasgowExts involves calling unsafePerformIO because it hides a side effect. Nevertheless, this trick is normally considered "safe enough" because the value is constant within any given run; it doesn't invalidate compiler optimisations, for example. However, GHC was later extended from a single-module compiler to a multi-module compiler. At this point the trick of using top-level constants for flags broke, because the flags may have different values when compiling different modules. So we had to refactor large amounts of code to pass around the flags explicitly. Perhaps you might argue that treating the flags as state in the first place, as would be natural in an imperative language, would have sidestepped the problem. To some extent this is true, although purely functional code has a number of other benefits, not least of which is that representing the flags by an immutable data structure means that the resulting code is already thread-safe and will run in parallel without modification. Developing the RTS GHC's runtime system presents a stark contrast to the compiler in many ways. There is the obvious difference that the runtime system is written in C rather than Haskell, but there are also considerations unique to the RTS that give rise to a different design philosophy: Every Haskell program spends a lot of time executing code in the RTS: 20--30% is typical, but characteristics of Haskell programs vary a lot and so figures greater or less than this range are also common. Every cycle saved by optimising the RTS is multiplied many times over, so it is worth spending a lot of time and effort to save those cycles. The runtime system is statically linked into every Haskell program (that is, unless dynamic linking is being used), so there is an incentive to keep it small. Bugs in the runtime system are often inscrutable to the user (e.g., "segmentation fault") and are hard to work around. For example, bugs in the garbage collector tend not to be tied to the use of a particular language feature, but arise when some complex combination of factors emerges at runtime. Furthermore, bugs of this kind tend to be non-deterministic (only occurring in some runs), and highly sensitive (tiny changes to the program make the bug disappear). Bugs in the multithreaded version of the runtime system present even greater challenges. It is therefore worth going to extra lengths to prevent these bugs, and also to build infrastructure to make identifying them easier. The symptoms of an RTS bug are often indistinguishable from two other kinds of failure: hardware failure, which is more common than you might think, and misuse of unsafe Haskell features like the FFI (Foreign Function Interface). The first job in diagnosing a runtime crash is to rule out these two other causes. The RTS is low-level code that runs on several different architectures and operating systems, and is regularly ported to new ones. Portability is important. Every cycle and every byte is important, but correctness is even more so. Moreover, the tasks performed by the runtime system are inherently complex, so correctness is hard to begin with. Reconciling these has lead us to some interesting defensive techniques, which we describe in the following sections. Coping With Complexity The RTS is a complex and hostile programming environment. In contrast to the compiler, the RTS has almost no type safety. In fact, it has even less type safety than most other C programs, because it is managing data structures whose types live at the Haskell level and not at the C level. For example, the RTS has no idea that the object pointed to by the tail of a cons cell is either [] or another cons: this information is simply not present at the C level. Moreover, the process of compiling Haskell code erases types, so even if we told the RTS that the tail of a cons cell is a list, it would still have no information about the pointer in the head of the cons cell. So the RTS code has to do a lot of casting of C pointer types, and it gets very little help in terms of type safety from the C compiler. So our first weapon in this battle is to avoid putting code in the RTS. Wherever possible, we put the minimum amount of functionality into the RTS and write the rest in a Haskell library. This has rarely turned out badly; Haskell code is far more robust and concise than C, and performance is usually perfectly acceptable. Deciding where to draw the line is not an exact science, although in many cases it is reasonably clear. For example, while it might be theoretically possible to implement the garbage collector in Haskell, in practice it is extremely difficult because Haskell does not allow the programmer precise control of memory allocation, and so dropping down to C for this kind of low-level task makes practical sense. There is plenty of functionality that can't be (easily) implemented in Haskell, and writing code in the RTS is not pleasant. In the next section we focus on one aspect of managing complexity and correctness in the RTS: maintaining invariants. Invariants, and Checking Them The RTS is full of invariants. Many of them are trivial and easy to check: for example, if the pointer to the head of a queue is NULL , then the pointer to the tail should also be NULL . The code of the RTS is littered with assertions to check these kinds of things. Assertions are our go-to tool for finding bugs before they manifest; in fact, when a new invariant is added, we often add the assertion before writing the code that implements the invariant. Some of the invariants in the runtime are far more difficult to satisfy, and to check. One invariant of this kind that pervades more of the RTS than any other is the following: the heap has no dangling pointers. Dangling pointers are easy to introduce, and there are many places both in the compiler and the RTS itself that can violate this invariant. The code generator could generate code that creates invalid heap objects; the garbage collector might forget to update the pointers of some object when it scans the heap. Tracking down these kinds of bugs can be extremely time-consuming (it is, however, one of the author's favourite activities!), because by the time the program eventually crashes, execution might have progressed a long way from where the dangling pointer was originally introduced. There are good debugging tools available, but they tend not to be good at executing the program in reverse. (Recent versions of GDB and the Microsoft Visual Studio debugger do have some support for reverse execution, however.) The general principle is: if a program is going to crash, it should crash as soon, as noisily, and as often as possible. (This quote comes from the GHC coding style guidelines, and was originally written by Alastair Reid, who worked on an early version of the RTS.) The problem is, the no-dangling-pointer invariant is not something that can be checked with a constant-time assertion. The assertion that checks it must do a full traversal of the heap! Clearly we cannot run this assertion after every heap allocation, or every time the GC scans an object (indeed, this would not even be enough, as dangling pointers don't appear until the end of GC, when memory is freed). So, the debug RTS has an optional mode that we call sanity checking. Sanity checking enables all kinds of expensive assertions, and can make the program run many times more slowly. In particular, sanity checking runs a full scan of the heap to check for dangling pointers (amongst other things), before and after every GC. The first job when investigating a runtime crash is to run the program with sanity checking turned on; sometimes this will catch the invariant violation well before the program actually crashes.