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11 min read

TL;DR: This blog post intends to sum up the why and how of cargo-cabal and hs-bindgen. If you鈥檙e looking for usage walkthroughs and code examples, check out project READMEs on GitHub!

N.B. quoted paragraphs in this article give straightforward motivation regarding some systems programming basic concepts. Feel free to skip them if you know you鈥檙e likely to be already comfortable with them ;)

9 min read

In a previous post we introduced GHC's new JavaScript backend, which allows the compilation of Haskell code into JavaScript. This is the first tutorial in a new series about the JavaScript backend. In this post, we'll build GHC as a JavaScript cross-compiler and run a trivial Haskell program in the browser.

We plan to write more of those blog post in the coming weeks and months as we add new features (e.g. support for "foreign exports" that will allow JavaScript code to call into Haskell code, support for Template Haskell, etc.). For now it relies on our "insider" knowledge (e.g. how the FFI works) that isn't well documented elsewhere. We do plan to add a chapter about the JavaScript backend in GHC's user guide, but for now your best chance is to look at GHCJS's documentation or at the source code.

Please note: this is a technology preview of the in-development JavaScript backend for GHC. Not all Haskell features are implemented, and bugs are expected. It is currently rather complicated for JavaScript code to call into Haskell code ("foreign exports" aren't implemented). GHC isn't a multi-target compiler yet, so a GHC executable built for a native platform (Linux/x86-64, Windows/x86-64, Darwin/AArch64...) as currently distributed (via ghcup, Stack, binary distributions, etc.) won't be able to produce JavaScript. Official prebuilt binary distributions are likely to remain unavailable until GHC gains multi-target support - requiring the JavaScript backend to be built from source even after the backend matures. That's why we start this post with the required steps to build yourself a GHC compiler capable of producing JavaScript.

20 min read

A new JavaScript backend was merged into GHC on November 30th, 2022! This means that the next release of GHC will be able to emit code that runs in web browsers without requiring any extra tools, enabling Haskell for both front-end and back-end web applications.

In this post, we, the GHC DevX team at IOG, describe the challenges we faced bringing GHCJS to GHC, how we overcame those challenges, and what's left to do. This post is rather long so we've provided these links in case you would like to skip ahead:

Take me to the future of GHCJS
Tell me what to expect
Show me the product roadmap
Tell me how I can help
Just show me how to hello world! (Skip to build instructions)

Why JavaScript? Or, the Big Picture.

To put it simply, the number of users on the internet is as low as it will ever be right now, and it is almost guaranteed that those users use JavaScript. At time of writing, JavaScript holds 97.3% of client-side programming market share (not to mention market share of front-end technologies). Furthermore, JavaScript is not going to disappear anytime soon. As more and more interactivity is pushed onto the internet, JavaScript will become more entrenched because of backwards compatibility, network effects and the amount of capital already devoted to it. JavaScript, like C and COBOL will be with us for the foreseeable future. This makes JavaScript an attractive target; it provides portability, allows us to capitalize on the massive investments in the language and platform, and essentially eliminates the risk that the we build our technology atop a disappearing or deprecating foundation.

WebAssembly is a promising target as well, and Tweag has just merged a WebAssembly backend into GHC (great work and congrats!). WebAssembly is not as ubiquitous as JavaScript yet, and has a harder time interacting with JavaScript directly. Hence, we believe that the WebAssembly and JavaScript backends provide different strengths, and it is to the Haskell community's benefit to have and support both code generation paths in GHC for different use cases and requirements.

15 min read

quickcheck-dynamic is a library jointly developed by Quviq and IOG, whose purpose is to leverage QuickCheck to test stateful programs against a Model. In other words, it's a Model-Based Testing tool. This article wants to be a gentle introduction to the use of quickcheck-dynamic for Model-Based Testing. It describes the overall approach, how the library works, and how it's being applied within IOG to improve the reach of our testing efforts.

9 min read

Introduction

I recently gave a short presentation about heap objects representation in GHCJS and hence in the upcoming JS backend for GHC. This post is a summary of the content.

Heap objects

GHC implements Haskell code evaluation by using graph reduction. As such Haskell programs compiled by GHC use the heap to store nodes of the graph to be reduced and utility nodes participating in graph reduction. These nodes are:

  • FUN: functions with their free variables as payload
  • THUNK: suspensions with their free variables as payload
  • PAP: partial application to a FUN. FUN closure and already applied arguments as payload.
  • IND: indirection to another heap object
  • BLACKHOLE: used to overwrite a THUNK when it is being evaluated

The heap is also used to store other values:

  • CON: boxed values (saturated constructor applications) with field values as payload
  • Other unlifted values: TSO, BCO, arrays, MutVar#, MVar#, TVar#, stacks, stack frames...

8 min read
  1. The Design Space
  2. GHCJSs FFI
  3. Lightweight safety checks
  4. Returning multiple values
  5. Changes in the FFI System for the JS Backend

Users of GHCJS enjoyed a rich FFI system for foreign JavaScript imports. However, this has changed during our adaptation of GHCJS to GHC 9.x. This short post goes over the GHCJS FFI system, the motivation for these changes and what the changes are. First, we must consider the design space of an FFI system.

The Design Space

FFI code is typically employed in high performance scenarios. Additionally, users of the FFI do not want to deal with the object language the compiler is compiling to. Instead, users want a simple way to call functions from the object language and use them in their own code as normal Haskell functions. However, users of the FFI system do tend to be power users, and so as a design principle we want to expose the tools they need to achieve their performance needs, whatever those needs may be. We can summarize these constraints as follows:

  1. The FFI must abstract the JavaScript backends infidelities away as much as possible. That is, users of the FFI should need to worry about the Int64# representation, but should also be able to simply follow standard patterns we have written in base.
  2. The FFI must provide tools to achieve high performance code, even if those tools require up front knowledge of the runtime system to use. However, these tools should not be in the path of least resistance to use the FFI system.
  3. The FFI must provide a lightweight specification that users program against for the JS backend to optimize the imported function and for good error messages for users.

GHCJSs FFI sets a high (qualitative) benchmark on these three constraints. Lets inspect them each in detail, in no particular order.

GHCJSs FFI

In GHCJS, a user could take advantage of JavaScript functions in their Haskell code using the GHCJSs FFI. However, the syntax was unique to GHCJS with place holder variables like one might see in perl, nix, or bash. For example, here is a foreign import from the base library for st_size:

-- base/System/Posix/Internal.hs
-- the JS FFI version
foreign import javascript unsafe "$r1 = h$base_st_size($1_1,$1_2); $r2 = h$ret1;"
st_size :: Ptr CStat -> IO Int64

The syntax is different from what we know and love in the normal Haskell world but the grammar is straightforward. We declare a foreign import from javascript, state that the import is unsafe or interruptible and then provide a string, h$base_fstat(...) for the code generator to use when compiling. Compare this with the C version:

-- base/System/Posix/Internal.hs
-- the C FFI version
foreign import ccall unsafe "HsBase.h __hscore_st_size"
st_size :: Ptr CStat -> IO Int64

And we see that they are similar. The only difference is the strange $n symbols in the referrent string. Contrast this with the C version, which simply declares a name.

These symbols are place holder variables with special meaning in GHCJS. There are two intractable reasons for the placeholder patterns. First, we require these patterns to work around the limitations of JavaScript as a backend (1). For example, consider the case where we need to return an Int64# from an imported foreign function. In C and Haskell this is not a problem because both can represent Int64# natively, however JavaScript only has native support for 32-bit values. Thus, to be able to return an Int64# we need to have a method to return two 32-bit numbers. Similarly, in order to apply a function to an Int64# that function must take at least two arguments, one for the high bits and one for the low. Second, the referrent string is untyped and can contain arbritrary JavaScript code. So placeholder patterns provide a simply and lightweight way for safety checks and eliminate classes of untyped, hard to understand errors. For example, consider an arity mismatch error between a function definition and call site. When this happens JavaScript happily continues processing with the return value from the function application defined as NaN (of course). Such arity conflicts can easily occur, especially when dealing with 64-bit values which require function arity assumptions.

Lightweight safety checks

Lightweight safety checks (3) are done by GHCJS by parsing the names of the place holder variables; each of which follows a specific naming convention. This convention is:

  • Argument types:
    • $n: Used for unary arguments, i.e., arguments which require only a single register.
    • $n_n: Used for binary arguments, i.e., arguments which require two registers.
    • $c: A continuation argument, only valid for interruptible foreign functions.
  • Return types:
    • $r: a unary return
    • $r1, $r2: a binary return
    • $r1, $r2, $r3_1, $r3_2: unboxed tuple return
  • Top level patterns:
    • "&value": simply emitted as value by the code generator
    • "someFunction": emitted as ret = someFunction(...), i.e., map the FFI to the result of the function call.
    • "$r = $1.f($2)": emitted as r1 = a1.f(a2), i.e., a combination of a function call and a property access.

With this standard GHCJS then parses the FFI referrent string to ensure that it conforms to this standard. If not then GHCJS can at least respond to the user with an ill-formatted FFI message and say precisely where the issue is. For example, it could respond that only half of an Int64# is returned based on the referrent string and the function type.

Returning multiple values

But what of performant code? GHCJS achieves performant FFI by not trying to abstract away from the runtime system. Instead, an advantage of GHCJSs FFI is that we can specify exactly which registers the foreign function should dump its results or even arbitrary global variables. This places more burden on the user of the FFI in specific scenarios, but crucially allows the FFI system to get out of the way of the user. The FFI system also exploits this capability to return multiple values from a single function call, which is a common need when compiling to JavaScript. For example, in the above code st_size is declared to return an IO Int64, the JavaScript handler h$base_st_size returns the Int64 using two registers $r1 and $r2, but does so through the use of a special purpose global variable called h$ret1:

function h$base_st_size(stat, stat_off) {
h$ret1 = (stat.i3[(stat_off>>2)+2]);
return (stat.i3[(stat_off>>2)+1]);
}

The function inputs a pointer and an offset. Pointers in GHCJS are simply pointers to ByteArrays so the function indexes into the ByteArray and retrieves and stores the lower 32-bits in h$ret1, then returns the higher 32-bits directly. These results are picked up by the FFI code, which performs assignment to set $r1 to the result of the function call (the higher 32-bits), and set $r2 to the value of h$ret1 (the lower 32-bits). Crucially, the runtime system needs to do nothing. The registers are already handled ready to be consumed by whatever the caller of the foreign function will do.

One might consider using a simpler design, which trades register juggling for a more straightforward representation such as a ByteArray which stores the Int64#. However, such a design would trade speed for implementation simplicity. If we passed ByteArrays then each foreign function would spend time wrapping and unwrapping the array to get the payload; clearly an undesirable outcome for high performance code.

Changes in the FFI System for the JS Backend

So we see that GHCJSs FFI system actually performs quite well in the design space. Power users are well supported and can leverage enough unsafety to bind global variables like h$ret1 and specific registers such as $r1. The system provides some lightweight checking through parsing. The nuances of the JavaScript platform are generally abstracted over and the FFI system is tuned for performance critical scenarios. So why change it?

The short answer is to hit deadlines. By skipping the FFI parsing the JS Backend team was able to produce a working (can output Hello World!, and compile GHCs boot libraries), integrated, JS backend in GHC faster than had we finished the FFI system.

For the time being, we have opted to replaced each foreign function call with a JavaScript fat arrow, for example:

foreign import javascript unsafe "(($1_1,$1_2) => { return h$base_st_size($1_1,$1_2); })"
st_size :: Ptr CStat -> IO Int64

Of course, this situation is untenable, as argued above, FFI code is assumed to be used in performance critical code, and thus any extra overhead, such as a function closure and consequent indirection, must be avoided. But fear not! In the near future well be overhauling the FFI system and returning it to its former glory.

2 min read

This is the July 2022 monthly update from the GHC DevX team at IOG.

JavaScript Backend for GHC

For a few months we have been merging GHCJS (Haskell to JavaScript compiler) into GHC. We set our first milestone to be the ability to compile and to run the usual "Hello World" program. This month we finally reached it!

We are now focusing on:

  • fixing failing tests in GHC's testsuite (~2800 unexpected failures). To do that, we have to implement new primops, to fix bugs we introduced while we ported the code from GHCJS, etc.

  • implementing support for the "js-sources" Cabal stanza in Hadrian. Currently the JS backend finds the JS sources required for the RTS and for base into explicitly defined location. It was only a stop-gap measure and we now need to implement proper support for user-provided JavaScript files.

  • documenting and refactoring the source code and making it similar to other GHC modules. As an example, GHCJS used the text package which isn't a boot package. Hence we first switched to use GHC's ShortText implementation and now we switched to a FastString based implementation.

  • adding back GHCJS's features that we haven't ported for some reasons (e.g. the compactor, TH, etc.).

You can follow our progress on our development branch here.

Blog posts

For the time being, we will focus blog post topics on GHCJS internals and related topics. A few of these blog posts are currently under review and should be published shortly.

6 min read

Introduction

I recently gave a short presentation on the workings of the GHCJS linker. This post is a summary of the content.

JavaScript "executables"

The task of a linker is collecting and organizing object files and resources into a loadable library or executable program. JavaScript can be run in various environments, for example the browser or node.js, and not in all of these the concept of an executable makes sense.

Therefore, when we link a Haskell program, we generate a jsexe directory filled with various files that allow us to run the JavaScript result:

FileDescription
out.jscompiled/linked Haskell code
out.frefs.*list of foreign calls from out.js
out.statssource code size origin statistics for out.js
lib.jsnon-Haskell code, from js-sources in packages and RTS. possibly preprocessed
rts.jsgenerated part of RTS (apply functions and similarly repetitive things)
runmain.jssingle line just starts main
all.jscomplete runnable program, created by combining out.js, lib.js, rts.js and runmain.js

Most of the work done by the linker is producing out.js, and that's what we'll be focusing on in the next sections.

Building out.js

The linker builds out.js by collecting all code reachable from main (and a few other symbols required by the RTS) and generating the required initialization code for all top-level data. The code is found in object files. These object files have the following structure:

SectionDescription
Headerversion number and offsets of other sections
String tableshared string table, referred to by Dependencies and Code, to avoid duplication in file and memory
DependenciesDependency data, internally between binding groups and externally to symbols in other object files
CodeCompiled Haskell code stored as serialized JavaScript AST and metadata. Code is organized in binding groups

The object files contain binding groups of mutually dependent bindings. These are the smallest units of code that can be linked. Each binding group has some associated metadata required for initialization of the heap objects in the group. The metadata contains for example constructor tags (e.g. 1 for Nothing, 2 for Just), the arity of functions and static reference tables.

From a high level, the procedure that the linker follows is this:

Step
Read object files from dependencies into memory
Decode dependency part of all object files in dependencies (includes reading the string tables)
Using dependency data, find all code reachable from main
Decode reachable binding groups
Render AST to JavaScript
Construct initializers from metadata

We avoid decoding (deserializing) the binding groups that do end up in the linked result to keep the memory consumption lower. Still the linker requires a lot of memory for larger programs, so we may need to make more improvements in the future.

The Compactor

The compactor is an optional link-time transformation step that reduces code size. It consists of a lightweight (i.e. no expensive operations like dataflow analysis) rewrite of the code contained in the object files. The compactor is disabled when linking with the -debug flag. There are a few steps involved.

Renaming private symbols

Haskell names are quite long by default: they need to be globally unique, hence they contain their defining unit-id and module name. For example: mtl-2.2.2-somehash-Control.Monad.State.Lazy.execState_go1 (special characters would be z-encoded but it isn't shown here).

Private symbols are only referred to from within the same module. It doesn't matter which JavaScript name we pick for them, as long as there is no overlap between the names from different modules. The compactor renames all the private symbols using a global sequence to ensure short names that do not overlap.

Block Initializer

Without the compactor, the linker generates an h$initObj initialization call (or h$o) call for each global Haskell heap value. The code for this can get quite big. The compactor collects all heap objects to be initialized in a single large array and encodes the metadata in a string. This makes the initialization code much more compact.

Deduplication

An optional step in the compactor is deduplication of code. When deduplication is enabled with the -dedupe flag, the compactor looks for functionally equivalent pieces of JavaScript in the output and merges them. This can result in a significant reduction of code size.

Incremental Linking

The linker supports building programs that are loaded incrementally. This is used for example for Template Haskell. The process that runs the Template Haskell stays alive during compilation of a whole module. When the first Template Haskell expression is compiled, it is linked against all its dependencies (including the RTS) and the resulting JavaScript code is sent over to be run in the evaluator process.

As subsequent Template Haskell expressions are evaluated in the same process, there is no need to load already loaded dependencies (including the RTS) again and it is much more efficient to avoid doing so. Therefore the linker keeps track of which dependencies have already been linked and each subsequent TH expression is only linked against dependencies that are not already loaded in the evaluator process.

It's also possible for users to use this functionality directly, with the -generate-base to create a "linker state" file along with the regular jsexe files. Another program can then be linked with -use-base=state_file, resulting in a program which leaves out everything already present in the first program.

Future Improvements

Memory consumption is the biggest problem in the linker at the moment. Possible ways to achieve this are compression, more efficient representation of the data structures or more incremental loading of the parts from the object files that we need.

In terms of functionality, we don't take advantage of JavaScript modules yet. It would be good if we could improve the linker to support linking a library as a JavaScript module. We should also consider making use of foreign export javascript for this purpose.

11 min read
  1. GHC Primitives
    1. The Easy Cases
    2. ByteArray#, MutableByteArray#, SmallArray#, MutableSmallArray#,
    3. Addr# and StablePtr#
    4. Numbers: The Involved Case
      1. Working with 64-bit Types
      2. Unwrapped Number Optimization
    5. But what about the other stuff!

One of the key challenges in any novel backend is representing GHC primitive types in the new backend. For JavaScript, this is especially tricky, as JavaScript only has 8 primitive types and some of those types, such as number do not directly map to any Haskell primitive type, such as Int8#. This post walks through the most important GHC primitives and describes our implementation for each in the JavaScript backend. This post is intended to be an explanation-oriented post, light on details, but just enough to understand how the system works.

GHC Primitives

There are 36 primtypes that GHC defines in primops.txt.pp:

  1. Char#
  2. Int8#, Int16#, Int32#, Int64#, Int#
  3. Word8#, Word16#, Word32#, Word64#, Word#
  4. Double#, Float#,
  5. Array#, MutableArray#,, SmallArray#, SmallMutableArray#
  6. ByteArray#, MutableByteArray#
  7. Addr#
  8. MutVar#, TVar#, MVar#,
  9. IOPort#, State#, RealWorld, ThreadId#
  10. Weak#, StablePtr#, StableName#, Compact#, BCO,
  11. Fun, Proxy#
  12. StackSnapshot#
  13. VECTOR

Some of these are unsupported in the JS-backend, such as VECTOR or lower priority such as StackSnapshot#. Well begin with the easy cases.

The Easy Cases

The easy cases are the cases that are implemented as JavaScript objects. In general, this is the big hammer used when nothing else will do. Well expand on the use of objectsespecially representing heap objectsin a future post, but for the majority of cases we mimic the STG-machine behavior for GHC heap objects using JavaScript heap objects. For example,

var someConstructor =
{ f = // entry function of the datacon worker
, m = 0 // garbage collector mark
, d1 = first arg // First data field for the constructor
, d2 = arity = 2: second arg // second field, or object containing the remaining fields
arity > 2: { d1, d2, ...} object with remaining args (starts with "d1 = x2"!)
}

This is the general recipe; we define a JavaScript object that contains properties which correspond to the entry function of the heap object; in this case that is the entry function, f for a constructor, some meta data for garbage collection m, and pointers to the fields of the constructor or whatever else the heap object might need. Using JavaScript objects allows straightforward translations of several GHC types. For example TVars and MVars:

// stg.js.pp
/** @constructor */
function h$TVar(v) {
TRACE_STM("creating TVar, value: " + h$collectProps(v));
this.val = v; // current value
this.blocked = new h$Set(); // threads that get woken up if this TVar is updated
this.invariants = null; // invariants that use this TVar (h$Set)
this.m = 0; // gc mark
this._key = ++h$TVarN; // for storing in h$Map/h$Set
#ifdef GHCJS_DEBUG_ALLOC
h$debugAlloc_notifyAlloc(this);
#endif
}

// stm.js.pp
function h$MVar() {
TRACE_SCHEDULER("h$MVar constructor");
this.val = null;
this.readers = new h$Queue();
this.writers = new h$Queue();
this.waiters = null; // waiting for a value in the MVar with ReadMVar
this.m = 0; // gc mark
this.id = ++h$mvarId;
#ifdef GHCJS_DEBUG_ALLOC
h$debugAlloc_notifyAlloc(this);
#endif
}

Notice that both implementations defined properties specific to the semantics of the Haskell type. JavaScript functions which create these objects follow the naming convention h$<something> and reside in Shim files. Shim files are JavaScript files that the JS-backend links against and are written in pure JavaScript. This allows us to save some compile time by not generating code which doesnt change, and decompose the backend into JavaScript modules.

This strategy is also how functions are implemented in the JS-backend. Function objects are generated by StgToJS.Expr.genExpr and StgToJS.Apply.genApp but follow this recipe:

var myFUN =
{ f = <function itself>
, m = <garbage collector mark>
, d1 = free variable 1
, d2 = free variable 2
}

To summarize; for most cases we write custom JavaScript objects which hold whatever machinery is needed as properties to satisfy the expected semantics of the Haskell type. This is the strategy that implements: TVar, MVar, MutVar and Fun.

ByteArray#, MutableByteArray#, SmallArray#, MutableSmallArray#,

ByteArray# and friends map to JavaScript's ArrayBuffer object. The ArrayBuffer object provides a fixed-length, raw binary data buffer. To index into the ArrayBuffer we need to know the type of data the buffer is expected to hold. So we make engineering tradeoff; we allocate typed views of the buffer payload once at buffer allocation time. This prevents allocations from views later when we might be handling the buffer in a hot loop, at the cost of slower initialization. For example, consider the mem.js.pp shim, which defines ByteArray#:

// mem.js.pp
function h$newByteArray(len) {
var len0 = Math.max(h$roundUpToMultipleOf(len, 8), 8);
var buf = new ArrayBuffer(len0);
return { buf: buf
, len: len
, i3: new Int32Array(buf)
, u8: new Uint8Array(buf)
, u1: new Uint16Array(buf)
, f3: new Float32Array(buf)
, f6: new Float64Array(buf)
, dv: new DataView(buf)
, m: 0
}
}

buf is the payload of the ByteArray#, len is the length of the ByteArray#. i3 to dv are the views of the payload; each view is an object which interprets the raw data in buf differently according to type. For example, i3 interprets buf as holding Int32, while dv interprets buf as a DataView and so on. The final property, m, is the garbage collector marker.

Addr# and StablePtr#

Addr# and StablePtr# are implemented as a pair of ByteArray# and an Int# offset into the array. Well focus on Addr# because StablePtr# is the same implementation, with the exception that the StablePtr# is tracked in the global variable h$stablePtrBuf. Addr#s do not have an explicit constructor, rather they are implicitly constructed. For example, consider h$rts_mkPtr which creates a Ptr that contains an Addr#:

function h$rts_mkPtr(x) {
var buf, off = 0;
if(typeof x == 'string') {

buf = h$encodeUtf8(x);
off = 0;
} else if(typeof x == 'object' &&
typeof x.len == 'number' &&
x.buf instanceof ArrayBuffer) {

buf = x;
off = 0;
} else if(x.isView) {

buf = h$wrapBuffer(x.buffer, true, 0, x.buffer.byteLength);
off = x.byteOffset;
} else {

buf = h$wrapBuffer(x, true, 0, x.byteLength);
off = 0;
}
return (h$c2(h$baseZCGHCziPtrziPtr_con_e, (buf), (off)));
}

The function does some type inspection to check for the special case on string. If we do not have a string then a Ptr, which contains an Addr#, is returned. The Addr# is implicitly constructed by allocating a new ArrayBuffer and an offset into that buffer. The object case is an idempotent check; if the input is already such a Ptr, then just return the input. The cases which do the work are the cases which call to h$wrapBuffer:

// mem.js.pp
function h$wrapBuffer(buf, unalignedOk, offset, length) {
if(!unalignedOk && offset && offset % 8 !== 0) {
throw ("h$wrapBuffer: offset not aligned:" + offset);
}
if(!buf || !(buf instanceof ArrayBuffer))
throw "h$wrapBuffer: not an ArrayBuffer"
if(!offset) { offset = 0; }
if(!length || length < 0) { length = buf.byteLength - offset; }
return { buf: buf
, len: length
, i3: (offset%4) ? null : new Int32Array(buf, offset, length >> 2)
, u8: new Uint8Array(buf, offset, length)
, u1: (offset%2) ? null : new Uint16Array(buf, offset, length >> 1)
, f3: (offset%4) ? null : new Float32Array(buf, offset, length >> 2)
, f6: (offset%8) ? null : new Float64Array(buf, offset, length >> 3)
, dv: new DataView(buf, offset, length)
};
}

h$wrapBuffer is a utility function that does some offset checks and performs the allocation for the typed views as described above.

Numbers: The Involved Case

Translating numbers has three issues. First, JavaScript has no concept of fixed-precision 64-bit types such as Int64# and Word64#. Second, JavaScript bitwise operators only support signed 32-bit values (except the unsigned right shift operator of course). Third, numbers are atomic types and do not require any special properties for correct semantics, thus using wrapping objects gains us nothing at the cost of indirection.

Working with 64-bit Types

To express 64-bit numerics, we simply use two 32-bit numbers, one to express the high bits, one for the low bits. For example, consider comparing two Int64#:

// arith.js.pp
function h$hs_ltInt64(h1,l1,h2,l2) {
if(h1 === h2) {
var l1s = l1 >>> 1;
var l2s = l2 >>> 1;
return (l1s < l2s || (l1s === l2s && ((l1&1) < (l2&1)))) ? 1 : 0;
} else {
return (h1 < h2) ? 1 : 0;
}
}

The less than comparison function expects four inputs, two for each Int64# in Haskell. The first number is represented by h1 and l1 (high and low), and similarly the second number is represented by h2 and l2. The comparison is straightforward, we check equivalence of our high bits, if equal then we check the lower bits while being careful with signedness. No surprises here.

For the bitwise operators we store both Word32# and Word# as 32-bit signed values, and then map any values greater or equal 2^31 bits to negative values. This way we stay within the 32-bit range even though in Haskell these types only support nonnegative values.

Unwrapped Number Optimization

The JS backend uses JavaScript values to represent both Haskell heap objects and unboxed values (note that this isn't the only possible implementation, see 1). As such, it doesn't require that all heap objects have the same representation (e.g. a JavaScript object with a "tag" field indicating its type) because we can rely on JS introspection for the same purpose (especially typeof). Hence this optimization consists in using a more efficient JavaScript type to represent heap objects when possible, and to fallback on the generic representation otherwise.

This optimization particularly applies to Boxed numeric values (Int, Word, Int8, etc.) which can be directly represented with a JavaScript number, similarly to how unboxed Int#, Word#, Int8#, etc. values are represented.

Pros:

  • Fewer allocations and indirections: instead of one JavaScript object with a field containing a number value, we directly have the number value.

Cons:

  • More complex code to deal with heap objects that can have different representations

The optimization is applicable when:

  1. We have a single data type with a single data constructor.
  2. The constructor holds a single field that can only be a particular type.

If these invariants hold then, we remove the wrapping object and instead refer to the value held by the constructor directly. Int8 is the simplest case for this optimization. In Haskell we have:

data Int8 = Int8 Int8#

Notice that this definition satisfies the requirements. A direct translation in the JS backend would be:

// An Int8 Thunk represented as an Object with an entry function, f
// and payload, d1.
var anInt8 = { d1 = <Int8# payload>
, f : entry function which would scrutinize the payload
}

We can operationally distinguish between a Thunk and an Int8 because these will have separate types in the StgToJS GHC pass and will have separate types (object vs number) at runtime. In contrast, in Haskell an Int8 may actually be a Thunk until it is scrutinized and then becomes the Int8 payload (i.e., call-by-need). So this means that we will always know when we have an Int8 rather than a Thunk and therefore we can omit the wrapper object and convert this code to just:

// no object, just payload
var anInt8 = = <Int8# payload>

For the interested reader, this optimization takes place in the JavaScript code generator module GHC.StgToJS.Arg, specifically the functions allocConStatic, isUnboxableCon, and primRepVt.

But what about the other stuff!

  • Char#: is represented by a number, i.e., the code point
  • Float#/Double#: Both represented as a JavaScript Double. This means that Float# has excess precision and thus we do not generate exactly the same answers as other platforms which are IEEE754 compliant. Full emulation of single precision Floats does not seem to be worth the effort as of writing. Our implementation represents these in a ByteArray#, where each Float# takes 4 bytes in the ByteArray#. This means that the precision is reduced to a 32-bit Float.

  1. An alternative approach would be to use some JS ArrayBuffers as memory blocks into which Haskell values and heap objects would be allocated. As an example this is the approach used by the Asterius compiler. The RTS would then need to be much more similar to the C RTS and the optimization presented in this section wouldn't apply because we couldn't rely on introspection of JS values.

4 min read

Introduction

I recently gave a short presentation on the topic of threads in GHCJS to the GHC team at IOG. This blog post is a summary of the content.

JavaScript and Threads

JavaScript is fundamentally single threaded. There are ways to share specific data between tasks but it's not possible to run multiple threads that have access to a shared memory space of JavaScript data.

The single JavaScript thread is often responsible for multiple tasks. For example a node.js server handles multiple simultaneous connections and a web application may be dealing with user input while downloading new data in the background.

This means that any single task should take care to never block execution of the other task. JavaScript's canonical answer is to use asynchronous programming. A function reading a file returns immediately without waiting for the file data to be loaded in memory. When the data is ready, a user-supplied callback is called to continue processing the data.

Haskell Threads

Concurrent Haskell supports lightweight threads through forkIO. These threads are scheduled on top of one more more operating system thread. A blocking foreign call blocks an OS thread but other lightweight threads can still run on other OS threads if available.

There is no built-in support for foreign calls with a callback in the style of JavaScript. Functions imported with foreign import ccall interruptible can be interrupted by sending an asynchronous exception to the corresponding lightweight thread.

Lightweight Threads in JavaScript

GHCJS implements lightweight threads on top of the single JavaScript thread. The scheduler switches between threads and handles synchronization through MVar and STM as expected from other Haskell platforms.

Foreign calls that don't block can be handled in the usual way. We extend the foreign function interface with a new type foreign import javascript interruptible that conveniently supports the callback mechanism used by JavaScript frameworks. The foreign call is supplied with an additional argument $c representing a callback to be called with the result when ready. From the Haskell side the corresponding lightweight thread is blocked until $c is called. This type of foreign call can be interrupted with an asynchronous exception to the lightweight Haskell thread.

By default, Haskell threads in the JS environment run asynchronously. A call to h$run returns immediately and starts the thread in the background. This works for tasks that does not require immediate actions. For situations that require more immediate action, such as dealing with event handler propagation, there is h$runSync. This starts a synchronous thread that is not interleaved with other task. If possible, the thread runs to completion before the call to h$runSync returns. If the thread blocks for any reason, such as waiting for an MVar or a foreign import javascript interruptible call, synchronous execution cannot complete. The blocking task is then either interrupted with an exception or the thread is "demoted" to a regular asynchronous thread.

Black Holes

When a Haskell value is evaluated, its heap object is overwritten by a black hole. This black hole marks the value as being evaluated and prevents other threads from doing the same. "black holing" can be done either immediately or "lazily", when the garbage collector is run. GHCJS implements immediate blackholing.

Black holes give rise to an interesting problem in the presence of synchronous and asynchronous threads. Typically if we use h$runSync, we want to have some guarantee that at least part of the task will run succesfully without blocking. For the most past it's fairly clear which parts of our task depends on potentially blocking IO or thread synchronization. But black holes throw a spanner in the works: Suddenly any "pure" data structure can be a source of blocking if it is under evaluation by another thread.

To regain some predictability and usability of synchronous threads, the h$runSync scheduler can run other Haskell threads in order to "clear" a black hole. The process ends all black holes have been cleared or when any of the black holes is impossible to clear because of a blocking situation.

This all happens transparantly to the caller of h$runSync, if the black holes could be cleared it appears as if they were never there.

Conclusion

We have lightweight Haskell threads in the single-threaded JavaScript environment and extend the foreign function interface to easily support foreign calls that depend on an asynchronous callback. This way, only the Haskell lightweight thread blocks.

By default, Haskell threads are asynchronous and run in the background: The scheduler interleaves the tasks and synchronization between threads. For situations that require immediate results or actions there are synchronous threads. Synchronous threads cannot block and are not interleaved with other tasks except when a black hole is encountered.

2 min read

This is the June 2022 monthly update from the GHC DevX team at IOG.

JavaScript Backend for GHC

For a few months we have been merging GHCJS (Haskell to JavaScript compiler) into GHC. We set our first milestone to be the ability to compile and to run the usual "Hello World" program. It turned out to be much more involved than we initially thought (requiring FFI support, etc.), but we should be getting there soon.

This month we have made the following progress:

  • Linking: GHCJS requires some functions to be directly implemented in JavaScript (e.g. the RTS, some low-level functions in base). We have added support for linking .js files. We've also added support for a preprocessing pass with CPP for .js.pp files.

  • js-sources: there is some ongoing work to load these external JavaScript files from installed libraries. Cabal provides a js-sources stanza for this, we need to adapt Hadrian to make use of it.

  • Binary vs Objectable: GHCJS used its own ByteString-based Objectable type-class: we replaced it with GHC's similar Binary type-class. Josh has published a blog post about their differences.

  • 64-bit primops: we've added support for 64-bit primops (Word64# and Int64# types). In GHCJS (GHC 8.10), these were still implemented as foreign function calls. It's no longer true on GHC head.

  • base library: added CPP as required to support the JS backend. Ported and converted FFI imports from GHCJS to use JavaScript fat arrows (we haven't implemented GHCJS's fancy import syntax yet).

Now we can compile and link the "HelloWorld" program. To reach the first milestone we only have to fix the remaining runtime errors.

You can follow our progress on our development branch here. We now rebase this branch every Friday to avoid lagging too much behind GHC head.

Haskell Optimization Handbook

The "Haskell Optimization Handbook" is an accepted proposal of the Haskell Foundation. Jeff has been steadily writing some initial material as per the project plan.

3 min read

This is the May 2022 monthly update from the GHC DevX team at IOG.

JavaScript Backend for GHC

For a few months we have been merging GHCJS (Haskell to JavaScript compiler) into GHC. We set our first milestone to be the ability to compile and to run the usual "Hello World" program. It turned out to be much more involved than we initially thought (requiring FFI support, etc.), but we should be getting there soon.

This month we have made the following progress:

  • RTS: we have modified Hadrian and rts.cabal in order to build a valid native rts unit that GHC can use, in particular containing appropriate header files.

  • linker: the JS linker has been hooked up with GHC's driver. We fixed several panics in the linker due to erroneous symbol generation code. These bugs were introduced while porting the code from the old 8.10 pretty-printing infrastructure to the newer one.

  • boot libraries: the JS backend can now build and link all the boot libraries. Note that we are not claiming that they are all usable yet. In particular complete FFI support is lacking, but the JS backend Hadrian build completes and so we can start using the produced JS cross-compiler.

  • levity polymorphism: building ghc-prim uncovered a lurking bug related to levity polymorphism. It wasn't noticed in GHCJS 8.10 because it is also related to the BoxedRep proposal that introduced a constructor application in a commonly used RuntimeRep.

  • sized literals: support for new sized literals have been added to the code generator.

Now that have achieved a build process that actually produces a JS cross compiler, we are confronting and fixing issues in the produced JavaScript code, such as adding, managing, and debugging CPP conditional compilation blocks in JS shim files. You can follow our progress on our development branch here.

External Static Plugins

GHC doesn't support plugins in cross-compilers #14335. Some time ago, we came up with a solution called "external static plugins" !7377. These are plugins that are directly loaded from shared libaries, bypassing the issue with usual plugins.

Our colleague Shea Levy confirmed that the approach works, backported it to GHC 8.10, and has been working on making it work in stage1 cross-compilers for Windows. Kudos for this work, Shea.

As the current user-interface based on environment variables isn't convenient, we have been working on adding new command-line flags to GHC instead. We expect to propose this for integration into GHC when the new interface will be fully implemented.

Blog posts

Inspired by our friends and colleagues at Well-Typed and Tweag, we have been starting to write blog posts for IOG's engineering blog. They will mostly be about stuff we are working on or that we are interested in. Feel free to send us feedback about these posts and to send us topics you would be interested to read about.

Haskell Optimization Handbook

The "Haskell Optimization Handbook" is an accepted proposal of the Haskell Foundation. Jeff has been working behind the scene to make this proposal concrete. More about this in the upcoming months.

11 min read

As part of the integration of GHCJS into GHC as a cross-compilation backend, we've converted the binary serialisation that GHCJS previously used, which was via its Objectable typeclass, into GHC's internal Binary typeclass representation. In doing this, we gain access to instances for serialising many of GHC's internal data types, and, importantly, we can reuse GHC's mechanism for serialising its Name and FastString types, which are written to lookup tables in order to maintain identity, as well as allowing for space savings on disk.

In this post, we will explain how the GHC Binary and GHCJS Objectable approaches work, and compare their tradeoffs.

How GHC Binary Works

Internally, GHC uses the Name data type to track the uniqueness of objects during compilation. Amongst information relating to the definition of a Name within the Haskell source, a Name also contains a Unique integer (the value of which is provided by the complation environment monad). Using this Unique integer, which is unpacked in Name's definition, we can make O(1) equality comparisons without following further memory references - allowing for this operation to be very quick, which will have a large effect on compilation performance given how often it is used.

FastString is used within GHC to store short, string-like data, and, similarly to Name, FastString uses a unique integer to allow for very fast equality comparisons. Primarily, FastString is used to represent variables and other definitions, and is used both in Name as the string-representation of a name with extra information attached, as well as directly, representing names that don't require this extra information, such as local variables.

In GHC's .hi interface files, Name and FastString are serialised differently compared to other data structures. They are written in the main data structure payload as indicies of a table, and these tables contain the actual string-like data of these types. So, an interface file might resemble:

  • Header
    • Magic number for recognising interface files
    • Pointer to Name symbol table
    • Pointer to FastString dictionary
  • Main data structure payload
  • Name symbol table
  • FastString dictionary

Importantly, the FastString dictionary must be written after the Name symbol table, because Names contain FastStrings, so writing the symbol table will expand the dictionary. Additionally, because we only have one buffer, and we don't know the size of the payload until it's written, the tables cannot be written in the header, and instead serialisation code must reserve space for the table pointers and jump back to write the pointers once the table locations are known.

During serialisation, GHC uses mutable data structures to store both the serialised binary buffer, as well as these tables:

data BinHandle
= BinMem { -- binary data stored in an unboxed array
bh_usr :: UserData, -- sigh, need parameterized modules :-)
_off_r :: !FastMutInt, -- the current offset
_sz_r :: !FastMutInt, -- size of the array (cached)
_arr_r :: !(IORef BinArray) -- the array (bounds: (0,size-1))
}

data UserData =
UserData {
-- for *deserialising* only:
ud_get_name :: BinHandle -> IO Name,
ud_get_fs :: BinHandle -> IO FastString,

-- for *serialising* only:
ud_put_nonbinding_name :: BinHandle -> Name -> IO (),
-- ^ serialize a non-binding 'Name' (e.g. a reference to another
-- binding).
ud_put_binding_name :: BinHandle -> Name -> IO (),
-- ^ serialize a binding 'Name' (e.g. the name of an IfaceDecl)
ud_put_fs :: BinHandle -> FastString -> IO ()
}

Here, we see that various functions are stored in the handle structure, to be later referenced by their respective types in their GHC.Utils.Binary.Binary typeclass instances. Notice that the instance of Binary Name references ud_put_nonbinding_name and ud_get_name. Similarly, the Binary FastString instance uses ud_put_fs and ud_get_fs.

class Binary a where
put_ :: BinHandle -> a -> IO ()
put :: BinHandle -> a -> IO (Bin a)
get :: BinHandle -> IO a

instance Binary FastString where
put_ bh f =
case getUserData bh of
UserData { ud_put_fs = put_fs } -> put_fs bh f

get bh =
case getUserData bh of
UserData { ud_get_fs = get_fs } -> get_fs bh

instance Binary Name where
put_ bh name =
case getUserData bh of
UserData{ ud_put_nonbinding_name = put_name } -> put_name bh name

get bh =
case getUserData bh of
UserData { ud_get_name = get_name } -> get_name bh

In GHC.Iface.Binary, helper types and functions are defined to store the Name symbol table and FastString dictionary in a mutable data structure. Here, putFastString is intended to be partially applied - passing it an appropriately initialised BinDictionary so that the resulting function can be stored in the us_put_fs field of the UserData. allocateFastString does the low-level work here, incrementing the index and modifying the mutable map (stored as a UniqFM, which is map keyed on types that contain Uniques - recalling that these are used for fast equality comparisons):

data BinDictionary = BinDictionary {
bin_dict_next :: !FastMutInt, -- The next index to use
bin_dict_map :: !(IORef (UniqFM FastString (Int,FastString)))
-- indexed by FastString
}

putFastString :: BinDictionary -> BinHandle -> FastString -> IO ()
putFastString dict bh fs = allocateFastString dict fs >>= put_ bh

allocateFastString :: BinDictionary -> FastString -> IO Word32
allocateFastString BinDictionary { bin_dict_next = j_r,
bin_dict_map = out_r} f = do
out <- readIORef out_r
let !uniq = getUnique f
case lookupUFM_Directly out uniq of
Just (j, _) -> return (fromIntegral j :: Word32)
Nothing -> do
j <- readFastMutInt j_r
writeFastMutInt j_r (j + 1)
writeIORef out_r $! addToUFM_Directly out uniq (j, f)
return (fromIntegral j :: Word32)

Later, in GHC.Iface.Binary, getWithUserData and putWithUserData will structure the header, and initialise the UserData functions to write to/read from mutable tables. Notice that we must first reserve header space for pointers to the lookup tables, as well as initialise the mutable tables, write these initialised structures to the UserData (for example, we see the previous putFastString partially applied here), then write the main payload, then write the lookup tables (Name symbol table first, because writing this can add to the FastString dictionary), and finally jump back to fill in the pointers to these tables:

putWithUserData :: Binary a => TraceBinIFace -> BinHandle -> a -> IO ()
putWithUserData traceBinIface bh payload = do
-- Remember where the dictionary pointer will go
dict_p_p <- tellBin bh
-- Placeholder for ptr to dictionary
put_ bh dict_p_p

-- Remember where the symbol table pointer will go
symtab_p_p <- tellBin bh
put_ bh symtab_p_p
-- Make some initial state
symtab_next <- newFastMutInt 0
symtab_map <- newIORef emptyUFM
let bin_symtab = BinSymbolTable {
bin_symtab_next = symtab_next,
bin_symtab_map = symtab_map }
dict_next_ref <- newFastMutInt 0
dict_map_ref <- newIORef emptyUFM
let bin_dict = BinDictionary {
bin_dict_next = dict_next_ref,
bin_dict_map = dict_map_ref }

-- Put the main thing,
bh <- return $ setUserData bh $ newWriteState (putName bin_dict bin_symtab)
(putName bin_dict bin_symtab)
(putFastString bin_dict)
put_ bh payload

-- Write the symtab pointer at the front of the file
symtab_p <- tellBin bh -- This is where the symtab will start
putAt bh symtab_p_p symtab_p -- Fill in the placeholder
seekBin bh symtab_p -- Seek back to the end of the file

-- Write the symbol table itself
symtab_next <- readFastMutInt symtab_next
symtab_map <- readIORef symtab_map
putSymbolTable bh symtab_next symtab_map
case traceBinIface of
QuietBinIFace -> return ()
TraceBinIFace printer ->
printer (text "writeBinIface:" <+> int symtab_next
<+> text "Names")

-- NB. write the dictionary after the symbol table, because
-- writing the symbol table may create more dictionary entries.

-- Write the dictionary pointer at the front of the file
dict_p <- tellBin bh -- This is where the dictionary will start
putAt bh dict_p_p dict_p -- Fill in the placeholder
seekBin bh dict_p -- Seek back to the end of the file

-- Write the dictionary itself
dict_next <- readFastMutInt dict_next_ref
dict_map <- readIORef dict_map_ref
putDictionary bh dict_next dict_map
case traceBinIface of
QuietBinIFace -> return ()
TraceBinIFace printer ->
printer (text "writeBinIface:" <+> int dict_next
<+> text "dict entries")

In summary, we see a number of structural characteristics of code using GHC's Binary implementation:

  • Use of a single buffer means that the lookup tables can't be written in the header, so we have to reserve space for table pointers in the header, and jump back once we know where they will be located in order to write the pointers to the buffer. Essentially, an ordering of file sections is enforced by the data dependencies of the payload containing Names and FastStrings, and Names containing FastStrings - which means these must be written in this order, but reading must be done in the reverse order, causing the need for pointers in the header.
  • Jumping around in binary buffers results in weakly enforced types and fiddly, code that Haskell's type system isn't able to help us debug
  • Must carry about read/write functions for the lookup table types (Name and FastString), which are undefined during the opposite serialisation stage, and are hard-coded into the handle, reducing extensibility.

How Objectable Works

In comparison, GHCJS previously involved using instances of the Objectable typeclass to serialise its interface files:

import qualified Data.Binary as DB

data SymbolTable
= SymbolTable !Int !(Map ShortText Int)
deriving (Show)

type PutSM = St.StateT SymbolTable DB.PutM -- FIXME: StateT isn't strict enough apparently
type PutS = PutSM ()
type GetS = ReaderT ObjEnv DB.Get

class Objectable a where
put :: a -> PutS
get :: GetS a
putList :: [a] -> PutS
putList = putListOf put
getList :: GetS [a]
getList = getListOf get

Here we see that GHCJS has opted for a different approach that avoids the mutable buffer by instead using Data.Binary instances that work via concatenating lazy ByteStrings. Additionally, the mutable tables are replaced with a State monad that holds the symbol table as a Map structure.

Because Data.Binary forms lazy ByteStrings, it's trivial to serialise the individual parts of the interface file and later concatenate these using ByteString's monoid instance - allowing for all of the sections of the file to be defined declaratively at the top-level of the function in order of their appearance within the file.

object'
:: ModuleName -- ^ module
-> SymbolTable -- ^ final symbol table
-> Deps -- ^ dependencies
-> [([ShortText],ByteString)] -- ^ serialized units and their exported symbols, the first unit is module-global
-> ByteString
object' mod_name st0 deps0 os = hdr <> symbs <> deps1 <> idx <> mconcat (map snd os)
where
hdr = putHeader (Header (moduleNameTag mod_name) (bl symbs) (bl deps1) (bl idx))
bl = fromIntegral . B.length
deps1 = putDepsSection deps0
(sti, idx) = putIndex st0 os
symbs = putSymbolTable sti

In summary, the use of multiple ByteString sections that are later concatenated offer several different structural characteristics compared to the use of a single mutable buffer:

  • The final ordering of the sections is flexible, because they are serialsied separately, so any data dependencies don't introduce ordering in the file - which we see in the where clause of object'
  • Types are more strongly enforced because imperative seekBin instructions aren't required. However, each section is still deserialised by taking a substring of the file to be read as that section type. Of course, all serialisation eventually results in raw binary, so the simplification of concatenating the sections into the final file without jumping around limits the places that bugs can hide
  • Visually, the ordering of the sections within the final file is very clear - we see in object' that every section is simply listed in order on one line, concatenated together.

Conclusion

Making use of GHC's existing infrastructure lets the GHCJS backend to make use of the FastString and Name data types, as well as allowing for the removal of a significant amount of now-redundant code.

Additionally, interface file generation using GHC's Binary appears to be very fast - for example, attempts to hide the handle behind a reader monad significantly reduce the compiler's performance as measured by CI. Speculatively, looking at the generated core, this could be because the optimiser has a much better time with the style of IO code that is used - rather than being a limitation of more abstacted approaches.

The comparison provided the GHCJS's old approach makes it clear that GHC's Binary implementation, while very useful, has potential to be improved in both readability and extensiblity. However, because CI has shown that serialisation performance has a significant effect on overall compilation performance, this tradeoff must be considered when making any changes. Potentially, these readability shortfalls in GHC's implementation might just be the result of legacy code, and so benchmarks of other approaches, such as Data.Binary, should be used to guide future work in improving the readability and flexibility of GHC's serialisation without sacrificing performance.

7 min read

Introduction

At IOG DevX we have been working on integrating various bits of GHCJS into GHC, with the goal of having a fully working JavaScript backend for the 9.6 release. For some parts this has mostly consisted of an update of the code to use the newer GHC API and dependencies. Other bits, like the Template Haskell runner, need more work.

This post gives an overview of the existing approaches for running Template Haskell in GHC based cross compilers and our plan for the JavaScript backend. Hopefully we can revisit this topic once all the work has been done, and see what exactly we ended up with.

The GHCJS Template Haskell Runner

When I first worked on Template Haskell (TH) support for GHCJS, there was no mechanism to combine Template Haskell with cross compilation in GHC.

Normally, Template Haskell is run by loading library code directly into the GHC process and using the bytecode interpreter for the current module. Template Haskell can directly access GHC data structures through the Q monad. Clearly this would not be possible for GHCJS: We only have JavaScript code available for the libraries and the organization of the JavaScript data structures is very different from what GHC uses internally.

So I had to look for an alternative. Running Template Haskell consists of two parts:

  1. loading/executing the TH code
  2. handling compiler queries from the TH code, for example looking up names or types

Running the TH code can be done by first compiling the Haskell to JavaScript and then using the JavaScript eval feature.

Template Haskell code can query the compiler using the Quasi typeclass. I noticed that none of the methods required passing around functions or complicated data structures, so it would be possible to serialize each request and response and send it to another process.

So I went ahead and implemented this approach with a script thrunner.js to load and start the code in a node.js server, a message type with serialization, and a new instance of the Quasi typeclass to handle the communication with the compiler via the messages. This is still what's in use by GHCJS to this day. Every time GHCJS encounters Template Haskell, it starts a thrunner process and the compiler communicates with it over a pipe.

After starting thrunner.js GHCJS sends the Haskell parts of the Template Haskell runnner to the script. This includes the runtime system and the implementation of the Quasi typeclass and communication protocol. After that, the TH session starts. A typical TH session looks as follows:

Compilerthrunner
RunTH THExp <js code> <source location>
LookupName (Just <name-string>)
LookupName' (Just <name>)
Reify <name>
Reify' <name-info>
RunTH' <result>
RunTH THDec <js code> <source location>
AddTopDecls <declarations>
AddTopDecls'
RunTH' <result>
FinishTH True
FinishTH' <memory-consumption>

Each message is followed up by a corresponding reply. For example, a LookupName' response follows a LookupName request and a RunTH message will eventually generate a RunTH' result. The first RunTH message contains the compiled JavaScript for the Template Haskell code, along with its dependencies. Each subsequent RunTH only includes dependencies that have not already been sent.

The thrunner process stays alive during the compilation of at least an entire module, allowing for persistent state (putQ/getQ).

The GHC External Interpreter

If we build a Haskell program with (cost centre) profiling, the layout of our data structures changes to include bookkeeping of cost centre information. This means that we need a special profiling runtime system to run this code.

What can we do if we want to run our profiled build in GHCi or Template Haskell? We cannot load compiled profiling libraries into GHC directly; its runtime system expects non-profiled code. We could use a profiled version of the compiler itself, but this would make all compilation very slow. Or we could somehow separate the profiled code of our own program from the non-profiled code in the compiler.

This was Simon Marlow's motivation for adapting the GHCJS thrunner approach, integrating in GHC and extending it it to support GHCi and bytecode. This functionality can be activated with the -fexternal-interpreter flag and has been available since GHC version 8.0.1. When the external interpreter is activated, GHC starts a separate process, iserv (customizable with the -pgmi flag) which has the role analogous to the thrunner script for GHCJS.

Over time, the iserv code has evolved with GHC and has been extended to include more operations. By now, there are quite a few differences in features:

Featurethrunneriserv
Template Haskell supportyesyes
GHCinoyes
Debuggernoyes
Bytecodenoyes
Object codethrough pipefrom file
Object code linkingcompileriserv process

thrunner is not quite as complete as iserv: It lacks GHCi and the debugger, and there is no bytecode support. But these features are not essential for basic Template Haskell.

Proxies and Bytecodes

We have now seen two systems for running Template Haskell code outside the compiler process: The original GHCJS thrunner and the extended GHC iserv.

Clearly it isn't ideal to have multiple "external interpreter" systems in GHC, therefore we plan to switch from thrunner to iserv for the upcoming JavaScript GHC backend. We don't need the debugger or GHCi support yet, but we do need to adapt to other changes in the infrastructure. So what does this mean in practice?

The biggest change is that we have to rework the linker: thrunner does not contain any linking logic by itself: GHCJS compiles everything to JavaScript and sends compiled code to the thrunner process, ready to be executed. In contrast, iserv has a loader for object and archive files. When dependencies need to be loaded into the interpreter, GHC just gives it the file name.

Another change is using the updated message types. In the thrunner session example above we could see that each message is paired with a response. For example a RunTH' response always follows a RunTH message, with possibly other messages in between. iserv has an interesting approach for the Message datatype: Instead of having pairs of data constructors for each message and its response, iserv has a GADT Message a, where the a type parameter indicates the expected response payload for each data constructor.

During development of the thrunner program it turned out to be very useful to save and replay Template Haskell sessions for debugging purposes. We'd like to do this again, but now saving the message in a readable/writable format. Since we're dealing with JavaScript, JSON appears to be the obvious choice.

Our plan is to have an iserv implementation that consists of a JavaScript part that runs in node.js and a proxy process to handle communication with GHC. The proxy process converts the messages between GHC's own (binary based) serialization format and JSON. The proxy process is relatively simple, but it does reveal one downside of the new GADT based message types: A proxy is stateful. We must always know which message we have sent to convert the response back from JSON to binary.

It's not yet known whether we will implement a full bytecode interpreter. We expect it to become clear during implementation whether we can get away without one early on.

Conclusion

We have seen how Template Haskell and GHCi code can be run outside the GHC process for profiling or cross compiling, with both the thrunner approach in GHCJS and the newer iserv in GHC.

We at IOG DevX are working on switching to the iserv infrastructure for the upcoming GHC JavaScript backend, which involves a substantial rewrite, mainly because of differences in linking. This is a work in progress, and we intend to revisit this topic in another blog post once the final design has been implemented.

2 min read

Welcome to the (rather late) April 2022 monthly update from the GHC DevX team at IOG. Since the last update we've continued work on the upcoming JavaScript backend for GHC. Unfortunately, we have nothing to show quite yet but that doesn't mean nothing has happened! On the contrary, we've made great progress and are close to that crucial first milestone hello world. Besides our work on the JavaScript backend, we were pleased to finally push through the Modularizing GHC paper that Sylvain has been working on for 2+ years! It causes quite the splash on the Haskell discourse and reddit, we recommend reading it if you haven't already (links below). Alright, enough introduction let's get into the update.

JavaScript Backend

We have made the following progresses in the implementation of a JavaScript backend for GHC (adapted from GHCJS):

  • linker: ported GHCJS's linker code into GHC. A lot of code was duplicated from GHC and slightly modified for GHCJS's needs, making the process far from trivial.

  • testsuite: fixed Hadrian to run GHC's testsuite with cross-compilers !7850. There are remaining issues though (see #21292).

  • build system: fixes for GHC's configure script were ported (e.g. support for the "ghcjs" target in config.sub). GHCJS's custom build script was integrated into configure.ac. We can now configure the build with: ./configure --target=js-unknown-ghcjs

  • TH: we have conducted some experiments to find the best way to bridge GHCJS's TH runner and GHC's external interpreter. This will be described in details in a future blog post.

  • FFI: basic support for JavaScript FFI has been ported from GHCJS to GHC. We haven't ported the JavaScript parser, so we have dropped the fancy import syntax (e.g. "$1.xyz"). It should be enough to build boot libraries and we will add JS parsing support later.

At this stage, we are working on building boot libraries and on supporting linking with the JS RTS.

Development happens in the following branch: https://gitlab.haskell.org/ghc/ghc/-/tree/wip/js-staging

Modularity paper

Sylvain, Jeffrey, and John Ericson (from Obsidian Systems) wrote a paper about "modularizing GHC" using domain-driven design.

We've got a lot of great feedback about it (expect a first revision soon). We also got a GHC contribution directly inspired by the paper (see !8160) which was very welcome!

18 min read

Table of Contents

Haskell is a great language camouflaged by lackluster tooling. This situation has led to well-known problems (who could forget Cabal hell?). A less discussed problem is what I will call the Black-box syndrome: It is hard to know exactly what the memory representation and runtime performance of my Haskell programs are1. Now black-box syndrome is not only a problem, it is also one of the nice features in the language since like all good abstractions it elides things Id rather not care about, at least most of the time. In other words, I am happy I dont have to do manual memory manipulation!

However, when I have my optimization hat on, I run face first into black-box syndrome. The crux of the problem is a tension between the need for observation during performance engineering and optimization, and the need to ship fast code. During development we want to be able to open up a system, see exactly how it is working, make tweaks, package it back up and test again. I want to be able to answer questions like Why is my executable this size?, Which code is a hot loop?, or When does my code do direct, known or unknown function calls?.

In order to answer these questions we need the ability to observe every part of that system as the machine experiences it, without this ability we have no way to make progress other than test, change some code, compile and test again in an ad-hoc manner. And therein lies the problem, most Haskell tooling is insufficient to provide the observability that we would like, instead the tooling often expects and requires us to make source code changes to our program or even recompile all of our libraries and code for a profiling way. This leads to the idea and the expectation in the Haskell community that Haskell programs are hard to optimize because the barrier to entry for optimization has artificially increased.

Csaba Hruska has recently been making headway in this area with his work on the GRIN compiler and an external STG interpreter. His STG interpreter (and patched ghc) exactly solve these problems and he has demonstrated dumping the entire call graph of large Haskell projects, filter to hot loops and finding unknown function calls in these graphs. If you havent seen his demo be sure to watch it, it is well worth your time.

This post is the first in a new blog series. In this blog series were going to kick the tires on the external STG interpreter see what it can do, and what we can uncover in some popular libraries by using it. In particular, Im interested in running it on projects Ive previously optimizedsuch as ghc itself, containers, unordered-containersusing the standard methods: ticky-ticky profiling, prof, flamegraphs, heap profiling, ghc-debug, cachegrind etc. This post, however, will be focused on setting up the patched ghc and interpreter on a NixOS system. My goals are threefold:

  1. Give an overview of the project and project layout to lower barrier to entry for the system.
  2. Give step by step instructions on setting up the interpreter on a nix-based system and provide a forked github repo for nix users. This should allow nix users to just git clone foo and nix-build (spoiler: it wont be that easy but still not hard.)
  3. Popularize Csabas project! It is a refreshing take on Haskell optimization and compilation.

Making sense of the project

The external STG interpreter is part of the GRIN compiler project. We are not doing anything with the GRIN compiler (yet!) and so we are only interested in The GHC whole compiler project. The whole-compiler-project has several sub-projects that well be building and using directly:

  • external-stg: This subproject provides utilites well be using, in particular mkfullpak
  • external-stg-interpreter: This is the actual STG interpreter. The good news is that this is independent of the rest of the project and can be built just like a regular Haskell executable
  • ghc-wpc: This is a fork of ghc-8.10.x (Im not sure exactly which version it forks to be honest) which we must build in order to use the external STG interpreter. Ghc-wpc serves as a frontend for the external-stg-interpreter.

Building a working external STG interpreter

The external STG interpreter can be built like any regular haskell executable. But in order to use the interpreter we have to build ghc-wpc. ghc-wpc is necessary because it serves as a frontend for the STG interpreter. It compiles a Haskell program like normal and then dumps an enriched STG IR to file. This file is then run through a utility gen-exe (gen-exe is an executable built in the external-stg-compiler sub-project) which picks up the compilation pipeline from the STG IR and creates an executable like we would expect from a normal compilation pipeline.

The major difference between this process and the usual compiler pipeline is that ghc-wpc leaves enough compiler information on disk for the rest of the tooling to consume, namely, in files with a *.o_stgbin (this is STG IR generated at compile time), and *.o_stgapp (project linker and dependency information) extension. Thus, once we build this custom ghc version we can use it to build the source code we wish to analyze and begin our optimization work.

For the rest of this tutorial Ill be referencing my fork of the ghc-whole-compiler-project that includes everything you need if you want to follow along, including .nix files for creating a nix-shell which will prepare a suitable environment to run the entire toolchain.

ghc.nix

The usual way to build ghc using a nix based system is with the ghc.nix project. Ghc.nix provides a default.nix with a suitable environment to run hadrian and build ghc. For ghc-wpc well need some special packages, and we need our boot compiler to be exactly ghc-8.3.3. The custom ghc.nix file is included in my fork, Ive taken the liberty to pin the nixpkgs to the right version for ghc-8.3.3. So lets begin:

Clone the forked repo:

$ git clone https://github.com/doyougnu/ghc-whole-program-compiler-project.git

$ cd ghc-whole-program-compiler-project

$ tree -L 1
.
鈹溾攢鈹 dist-newstyle
鈹溾攢鈹 external-stg
鈹溾攢鈹 external-stg-compiler
鈹溾攢鈹 external-stg-interpreter
鈹溾攢鈹 ghc.nix.wpc
鈹溾攢鈹 ghc-wpc
鈹溾攢鈹 lambda
鈹溾攢鈹 mod-pak
鈹溾攢鈹 README.md
鈹溾攢鈹 shell.nix
鈹溾攢鈹 stack.yaml
鈹斺攢鈹 stack.yaml.lock

Youll find the patched ghc.nix included (ghc.nix.wpc) and a shell.nix for a nix-shell. The shell.nix file simply references ghc.nix.wpc/default.nix with the appropriate options:

$ cat shell.nix
import (./ghc.nix.wpc/default.nix) {
useClang = true;
withHadrianDeps = true;
withIde = false;
withLlvm = true;
}

Building ghc-wpc

Now we can enter a nix-shell and build ghc-wpc:

$ pwd
/home/doyougnu/programming/haskell/ghc-whole-program-compiler-project

$ nix-shell shell.nix # or just nix-shell
trace: checking if /home/doyougnu/programming/haskell/ghc-whole-program-compiler-project/hadrian/hadrian.cabal is present: no
Recommended ./configure arguments (found in $CONFIGURE_ARGS:
or use the configure_ghc command):

--with-gmp-includes=/nix/store/sznfxigwvrvn6ar3nz3f0652zsld9xqj-gmp-6.2.0-dev/include
--with-gmp-libraries=/nix/store/447im4mh8gmw85dkrvz3facg1jsbn6c7-gmp-6.2.0/lib
--with-curses-includes=/nix/store/84g84bg47xxg01ba3nv0h418v5v3969n-ncurses-6.1-20190112-dev/include
--with-curses-libraries=/nix/store/xhhkr936b9q5sz88jp4l29wljbbcg39k-ncurses-6.1-20190112/lib
--with-libnuma-includes=/nix/store/bfrcskjspk9a179xqqf1q9xqafq5s8d2-numactl-2.0.13/include
--with-libnuma-libraries=/nix/store/bfrcskjspk9a179xqqf1q9xqafq5s8d2-numactl-2.0.13/lib
--with-libdw-includes=/nix/store/sv6f05ngaarba50ybr6fdfc7cciv6nbv-elfutils-0.176/include
--with-libdw-libraries=/nix/store/sv6f05ngaarba50ybr6fdfc7cciv6nbv-elfutils-0.176/lib
--enable-dwarf-unwind

[nix-shell:~/programming/haskell/ghc-whole-program-compiler-project]$

Now we need to cd into ghc-wpc and tweak the hadrian build.

MAJOR CONSTRAINT: You must build ghc-wpc with hadrian/build-stack, if you build in any other way youll run into shared object errors, see this ticket for details.

So in order to build ghc-wpc with stack well have to tweak the stack.yaml file. You must do this since it is not included in the fork:

Quick side note: To make the formatting nicer I truncate nix-shell:~/foo/bar/baz/ghc-whole-program-compiler-project to just ..., so nix-shell:.../ghc-wpc is equivalent to ~/path/to/ghc-whole-compiler-project/ghc-wpc.

[nix-shell:...]$ cd ghc-wpc/hadrian/

[nix-shell:.../ghc-wpc/hadrian]$ cat stack.yaml
resolver: lts-15.5

packages:
- '.'
- 'GHC-Cabal'

system-ghc: true

nix:
enable: true
shell-file: ../../shell.nix

The changes are: (1) tell stack we are using nix, and (2) reference the shell.nix file which points to ghc.wpc.nix at the root of the project, i.e., ghc-whole-program-compiler-project/shell.nix.

Now we should be able to begin our build, return to the root of ghc-wpc and run the following:

[nix-shell:.../ghc-wpc/hadrian]$ cd ..

[nix-shell:.../ghc-wpc]$ ./boot && ./configure

[nix-shell:.../ghc-wpc]$ hadrian/build-stack -j

and go get some coffee since this will take some time. Once it finishes you should have the ghc-wpc binary in _build/stage1/bin

[nix-shell:.../ghc-wpc]$ ls -l _build/stage1/bin/
total 8592
-rwxr-xr-x 1 doyougnu users 1843752 Apr 29 23:01 ghc
-rw-r--r-- 1 doyougnu users 11082 Apr 29 23:01 ghc.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 660128 Apr 29 22:50 ghc-pkg
-rw-r--r-- 1 doyougnu users 9977 Apr 29 22:50 ghc-pkg.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 4624680 Apr 29 23:01 haddock
-rw-r--r-- 1 doyougnu users 16883 Apr 29 23:01 haddock.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 49344 Apr 29 22:25 hp2ps
-rw-r--r-- 1 doyougnu users 2504 Apr 29 22:25 hp2ps.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 716440 Apr 29 22:35 hpc
-rw-r--r-- 1 doyougnu users 9959 Apr 29 22:35 hpc.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 738544 Apr 29 22:35 hsc2hs
-rw-r--r-- 1 doyougnu users 10264 Apr 29 22:35 hsc2hs.dyn_o_ghc_stgapp
-rwxr-xr-x 1 doyougnu users 58384 Apr 29 22:34 runghc
-rw-r--r-- 1 doyougnu users 8864 Apr 29 22:34 runghc.dyn_o_ghc_stgapp

Notice that this build dumped *.<way>_o_ghc_stgapp files!

Building the stg tooling

Now that we have a working ghc-wpc we need to build the rest of the project by pointing stack to the ghc-wpc binary in ghc-wpc/_build/stage1/bin. That is, we must change the ghc-whole-program-compiler-project/stack.yaml file:

[nix-shell:~/programming/haskell/ghc-whole-program-compiler-project]$ cat stack.yaml
resolver: lts-16.13

allow-newer: true

packages:
- 'external-stg-compiler'
- 'external-stg'

ghc-options:
"$everything": -fno-stgbin -fno-stgapp -optcxx-std=c++17

extra-deps:
- async-pool-0.9.1@sha256:4015140f896c3f1652b06a679b0ade2717d05557970c283ea2c372a71be2a6a1,1605
- souffle-haskell-1.1.0
- zip-1.7.0


# use custom ext-stg whole program compiler GHC
compiler: ghc-8.11.0
skip-ghc-check: true

nix:
enable: false


# use local GHC (for development)
system-ghc: true
extra-path:
- /home/doyougnu/programming/haskell/ghc-whole-program-compiler-project/ghc-wpc/_build/stage1/bin

# DEBUG INFO
#dump-logs: all
#build:
# keep-tmp-files: true
# cabal-verbose: true

The changes are: (1) set compiler: ghc-8.11.0 (the ghc-wpc fork), (2) set skip-ghc-check: true so that stack doesnt complain about the ghc version, (3) set nix.enable: false, confusingly if you leave this as true then stack will try to use nixpkgs to get a ghc binary, but we want it to use our local binary so we disable this even though well still be in our original nix-shell (4) set system-path: true to tell stack we will be using a ghc we have on our system, and finally (5) set extra-path: <path-to-ghc-wpc-binary>.

Now we can run stack and install the stg tooling:

[nix-shell:...]$ stack --stack-root `pwd`/.stack-root install
Trouble loading CompilerPaths cache: UnliftIO.Exception.throwString called with:

Compiler file metadata mismatch, ignoring cache
Called from:
throwString (src/Stack/Storage/User.hs:277:8 in stack-2.7.5-9Yv1tjrmAU3JiZWCo86ldN:Stack.Storage.User)

WARNING: Ignoring tagged's bounds on template-haskell (>=2.8 && <2.17); using template-haskell-2.17.0.0.
Reason: allow-newer enabled.
WARNING: Ignoring aeson's bounds on template-haskell (>=2.9.0.0 && <2.17); using template-haskell-2.17.0.0.
Reason: allow-newer enabled.
WARNING: Ignoring th-abstraction's bounds on template-haskell (>=2.5 && <2.17); using template-haskell-2.17.0.0.
Reason: allow-newer enabled.
WARNING: Ignoring unliftio-core's bounds on base (>=4.5 && <4.14); using base-4.14.0.0.
Reason: allow-newer enabled.
WARNING: Ignoring souffle-haskell's bounds on megaparsec (>=7.0.5 && <8); using megaparsec-8.0.0.
stack --stack-root `pwd`/.stack-root install
... # bunch of output
...
...
Copied executables to /home/doyougnu/.local/bin:
- dce-fullpak
- ext-stg
- fullpak
- gen-exe
- gen-exe2
- gen-obj
- gen-obj2
- mkfullpak
- show-ghc-stg

Warning: Installation path /home/doyougnu/.local/bin not found on the PATH environment variable.

You can add ~/.local/bin to your PATH if you want, Ill just be directly referencing these binaries as we go.

Building the external-stg-interpreter

We are almost all done, all that is left is to build the external-stg-interpreter and run a small script that links everything together into a shared object for the interpreter. So:

[nix-shell:...]$ cd external-stg-interpreter/

[nix-shell:.../external-stg-interpreter]$ stack install
... # bunch of output
...
Copied executables to /home/doyougnu/.local/bin:
- ext-stg
- ext-stg-interpreter
- fullpak
- mkfullpak

Warning: Installation path /home/doyougnu/.local/bin not found on the PATH environment variable.

Now we have our ext-stg-interpreter built! There are a few caveats I want to point out here. Ive modified ghc-whole-program-compiler-project/external-stg-interpreter/stack.yaml to load the right packages and use nix:

[nix-shell:.../external-stg-interpreter]$ cat stack.yaml
resolver: lts-16.13

packages:
- '.'
- 'external-stg'

extra-deps:
- souffle-haskell-2.1.0
- primitive-0.7.1.0
- zip-1.7.0

nix:
enable: true
packages: [ zlib, libffi, pkg-config, bzip2 ]

Notice the nix: block. We could have just as easily built this using nix directly or using our shell.nix file.

Linking the external-stg-interpreter

The only task left is to link into a shared object library called libHSbase-4.14.0.0.cbits.so. To do that we need to use the script called, c, in ghc-whole-program-compiler-project/external-stg-interpreter/data. This script is a bit of a hack, it generates the shared object file so that we can link the symbols requested by the C FFI in base, but it populates those functions with our replacements, which do absolutely nothing. For example, we supply a fake garbage collect:

// in .../external-stg-interpreter/data/cbits.so-script/c-src/fake_rts.c
...
void performGC(void) {
}

void performMajorGC(void) {
}
...

This works because we won't be using the runtime system at all, we'll be using the external STG interpreter instead, however we still need to provide these symbols in order to link. **MAJOR NOTE: this file must be next to any *.fullpak file youll be running the interpreter on** or else youll get an undefined symbol error during linking, for example:

[nix-shell:.../external-stg-interpreter/data]$ ls
cbits.so-script ghc-rts-base.fullpak minigame-strict.fullpak

### notice no .so file
[nix-shell:.../external-stg-interpreter/data]$ ~/.local/bin/ext-stg-interpreter ghc-rts-base.fullpak
ext-stg-interpreter: user error (dlopen: ./libHSbase-4.14.0.0.cbits.so: cannot open shared object file: No such file or directory)

## we error'd out because it was missing, also
## if you get this error then you have an old cbits.so file and need to rerun the c script
[nix-shell:.../external-stg-interpreter/data]$ ~/.local/bin/ext-stg-interpreter ghc-rts-base.fullpak
ext-stg-interpreter: user error (dlopen: ./libHSbase-4.14.0.0.cbits.so: undefined symbol: getProcessElapsedTime)

To link the interpreter we need to run c in the data/cbits.so-script sub-folder:

[nix-shell:.../external-stg-interpreter]$ cd data/cbits.so-script/

[nix-shell:.../external-stg-interpreter/data/cbits.so-script]$ ls
ar c cbits-rts.dyn_o c-src libHSbase-4.14.0.0.cbits.so stub-base.dyn_o

[nix-shell:.../external-stg-interpreter/data/cbits.so-script]$ ./c
++ ls ar/libHSbase-4.14.0.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSbindings-GLFW-3.3.2.0-Jg9TvsfYUZwD0ViIP0H2Tz-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSbytestring-0.10.9.0-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHScriterion-measurement-0.1.2.0-73BCI2Fnk7qE8QjjTa1xNa-ghc8.11.0.20210324.dyn_o_cbits.a ar/libHSghc-8.11.0.20210306-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSGLUT-2.7.0.15-1pzTWDEZBcYHcS36qZ2lpp-ghc8.11.0.20201112.dyn_o_cbits.a ar/libHSGLUT-2.7.0.15-1pzTWDEZBcYHcS36qZ2lpp-ghc8.11.0.20210324.dyn_o_stubs.a ar/libHShashable-1.3.0.0-Kn7aNSFvzgo2qY16wYzuCX-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSinteger-gmp-1.0.3.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSlambdacube-quake3-engine-0.1.0.0-7CKLP3Rqgq0PR81lhlwlR-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSmersenne-random-pure64-0.2.2.0-ExYg8DmthtrLG9JevQbt2m-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSOpenGLRaw-3.3.4.0-5vXBlmbOM3AIT7GRYfpE3o-ghc8.11.0.20201112.dyn_o_cbits.a ar/libHSprimitive-0.7.0.1-2k3g9qX0zz16vEv34R307m-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSprocess-1.6.8.2-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHStext-1.2.4.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSunix-2.7.2.2-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSunix-2.7.2.2-ghc8.11.0.20210220.dyn_o_stubs.a ar/libHSzlib-0.6.2.1-1I6DmfbLEyTBgDZI7SbZfW-ghc8.11.0.20210306.dyn_o_stubs.a
++ ls stub-base.dyn_o/Blank_stub.dyn_o stub-base.dyn_o/ClockGetTime_stub.dyn_o stub-base.dyn_o/Internals_stub.dyn_o stub-base.dyn_o/RUsage_stub.dyn_o
++ ls cbits-rts.dyn_o/StgPrimFloat.dyn_o cbits-rts.dyn_o/TTY.dyn_o
++ ls c-src/fake_rts.c c-src/hack.c c-src/hschooks.c
+ gcc -o libHSbase-4.14.0.0.cbits.so -shared -Wl,--whole-archive ar/libHSbase-4.14.0.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSbindings-GLFW-3.3.2.0-Jg9TvsfYUZwD0ViIP0H2Tz-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSbytestring-0.10.9.0-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHScriterion-measurement-0.1.2.0-73BCI2Fnk7qE8QjjTa1xNa-ghc8.11.0.20210324.dyn_o_cbits.a ar/libHSghc-8.11.0.20210306-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSGLUT-2.7.0.15-1pzTWDEZBcYHcS36qZ2lpp-ghc8.11.0.20201112.dyn_o_cbits.a ar/libHSGLUT-2.7.0.15-1pzTWDEZBcYHcS36qZ2lpp-ghc8.11.0.20210324.dyn_o_stubs.a ar/libHShashable-1.3.0.0-Kn7aNSFvzgo2qY16wYzuCX-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSinteger-gmp-1.0.3.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSlambdacube-quake3-engine-0.1.0.0-7CKLP3Rqgq0PR81lhlwlR-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSmersenne-random-pure64-0.2.2.0-ExYg8DmthtrLG9JevQbt2m-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSOpenGLRaw-3.3.4.0-5vXBlmbOM3AIT7GRYfpE3o-ghc8.11.0.20201112.dyn_o_cbits.a ar/libHSprimitive-0.7.0.1-2k3g9qX0zz16vEv34R307m-ghc8.11.0.20210306.dyn_o_cbits.a ar/libHSprocess-1.6.8.2-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHStext-1.2.4.0-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSunix-2.7.2.2-ghc8.11.0.20210220.dyn_o_cbits.a ar/libHSunix-2.7.2.2-ghc8.11.0.20210220.dyn_o_stubs.a ar/libHSzlib-0.6.2.1-1I6DmfbLEyTBgDZI7SbZfW-ghc8.11.0.20210306.dyn_o_stubs.a -Wl,--no-whole-archive stub-base.dyn_o/Blank_stub.dyn_o stub-base.dyn_o/ClockGetTime_stub.dyn_o stub-base.dyn_o/Internals_stub.dyn_o stub-base.dyn_o/RUsage_stub.dyn_o cbits-rts.dyn_o/StgPrimFloat.dyn_o cbits-rts.dyn_o/TTY.dyn_o -fPIC c-src/fake_rts.c c-src/hack.c c-src/hschooks.c -lm -lgmp -ltinfo -lGL -lX11 -lXi -lXrandr -lXxf86vm -lXcursor -lXinerama -lpthread

This will produce libHSbase-4.14.0.0.cbits.so in the immediate directory:

[nix-shell:.../external-stg-interpreter/data/cbits.so-script]$ ls -l
total 984
drwxr-xr-x 2 doyougnu users 4096 Apr 27 14:10 ar
-rwxr-xr-x 1 doyougnu users 300 Apr 27 14:10 c
drwxr-xr-x 2 doyougnu users 4096 Apr 27 14:10 cbits-rts.dyn_o
drwxr-xr-x 2 doyougnu users 4096 Apr 27 14:10 c-src
-rwxr-xr-x 1 doyougnu users 986008 Apr 30 11:50 libHSbase-4.14.0.0.cbits.so ## <----- new
drwxr-xr-x 2 doyougnu users 4096 Apr 27 14:10 stub-base.dyn_o

Now we can test our interpreter by running it on the *.fullpak files in external-stg-interpreter/data:

[nix-shell:.../external-stg-interpreter/data/cbits.so-script]$ cd ..

[nix-shell:.../external-stg-interpreter/data]$ ls
cbits.so-script ghc-rts-base-call-graph-summary ghc-rts-base-call-graph.tsv ghc-rts-base.fullpak libHSbase-4.14.0.0.cbits.so minigame-strict.fullpak

## remove the old .so file
[nix-shell:.../external-stg-interpreter/data]$ rm libHSbase-4.14.0.0.cbits.so

## soft-link to the one we just built
[nix-shell:.../external-stg-interpreter/data]$ ln -s cbits.so-script/libHSbase-4.14.0.0.cbits.so libHSbase-4.14.0.0.cbits.so

[nix-shell:.../external-stg-interpreter/data]$ ls -l
total 79220
drwxr-xr-x 6 doyougnu users 4096 Apr 30 11:50 cbits.so-script
-rw-r--r-- 1 doyougnu users 48 Apr 30 11:47 ghc-rts-base-call-graph-summary
-rw-r--r-- 1 doyougnu users 28238 Apr 30 11:47 ghc-rts-base-call-graph.tsv
-rw-r--r-- 1 doyougnu users 22450708 Apr 27 14:10 ghc-rts-base.fullpak
lrwxrwxrwx 1 doyougnu users 43 Apr 30 11:55 libHSbase-4.14.0.0.cbits.so -> cbits.so-script/libHSbase-4.14.0.0.cbits.so ### <---- new
-rw-r--r-- 1 doyougnu users 58630129 Apr 27 14:10 minigame-strict.fullpak

[nix-shell:.../external-stg-interpreter/data]$ ~/.local/bin/ext-stg-interpreter ghc-rts-base.fullpak
hello
hello
ssHeapStartAddress: 53522
ssTotalLNECount: 69
ssClosureCallCounter: 360
executed closure id count: 114
call graph size: 150

[nix-shell:.../external-stg-interpreter/data]$ ls -l
total 79220
drwxr-xr-x 6 doyougnu users 4096 Apr 30 11:50 cbits.so-script
-rw-r--r-- 1 doyougnu users 48 Apr 30 11:56 ghc-rts-base-call-graph-summary ### <---- interpreter output
-rw-r--r-- 1 doyougnu users 28238 Apr 30 11:56 ghc-rts-base-call-graph.tsv ### <---- interpreter output
-rw-r--r-- 1 doyougnu users 22450708 Apr 27 14:10 ghc-rts-base.fullpak
lrwxrwxrwx 1 doyougnu users 43 Apr 30 11:55 libHSbase-4.14.0.0.cbits.so -> cbits.so-script/libHSbase-4.14.0.0.cbits.so
-rw-r--r-- 1 doyougnu users 58630129 Apr 27 14:10 minigame-strict.fullpak

And it works, we have two new files, <foo>-call-graph-summary and <foo>-call-graph.tsv which we can analyze to inspect the behavior of our program (more on this later).

The whole setup process on a demo

That was a rather involved example, to make clear the dependencies and steps required to run this on your own code the rest of this tutorial will run the interpreter on two of Csabas demos from his skillshare talk. First lets grab the code:

$ pwd
/home/doyougnu/programming/haskell

$ git clone https://github.com/grin-compiler/ext-stg-interpreter-presentation-demos.git

$ ls
ext-stg-interpreter-presentation-demos ghc-whole-program-compiler-project ..

Now well run the first demo which is a simply fold over a list:

$ nix-shell ghc-whole-program-compiler-project/shell.nix
trace: checking if /home/doyougnu/programming/haskell/hadrian/hadrian.cabal is present: no
Recommended ./configure arguments (found in $CONFIGURE_ARGS:
or use the configure_ghc command):

--with-gmp-includes=/nix/store/sznfxigwvrvn6ar3nz3f0652zsld9xqj-gmp-6.2.0-dev/include
--with-gmp-libraries=/nix/store/447im4mh8gmw85dkrvz3facg1jsbn6c7-gmp-6.2.0/lib
--with-curses-includes=/nix/store/84g84bg47xxg01ba3nv0h418v5v3969n-ncurses-6.1-20190112-dev/include
--with-curses-libraries=/nix/store/xhhkr936b9q5sz88jp4l29wljbbcg39k-ncurses-6.1-20190112/lib
--with-libnuma-includes=/nix/store/bfrcskjspk9a179xqqf1q9xqafq5s8d2-numactl-2.0.13/include
--with-libnuma-libraries=/nix/store/bfrcskjspk9a179xqqf1q9xqafq5s8d2-numactl-2.0.13/lib
--with-libdw-includes=/nix/store/sv6f05ngaarba50ybr6fdfc7cciv6nbv-elfutils-0.176/include
--with-libdw-libraries=/nix/store/sv6f05ngaarba50ybr6fdfc7cciv6nbv-elfutils-0.176/lib
--enable-dwarf-unwind

[nix-shell:~/programming/haskell]$ cd ext-stg-interpreter-presentation-demos/demo-01-tsumupto/

[nix-shell:~/programming/haskell/ext-stg-interpreter-presentation-demos/demo-01-tsumupto]$ ../../ghc-whole-program-compiler-project/ghc-wpc/_build/stage1/bin/ghc -O2 tsumupto.hs
[1 of 1] Compiling Main ( tsumupto.hs, tsumupto.o )
Linking tsumupto ...
$ cd ext-stg-interpreter-presentation-demos/demo-01-tsumupto

$ ls
tsumupto tsumupto.hi tsumupto.hs tsumupto.o tsumupto.o_ghc_stgapp tsumupto.o_modpak

Note, that we have two new files: *.o_ghc_stgapp and .o_modpak as a result of building with ghc-wpc. If you try to run this from outside the nix-shell youll get an error about missing mkmodpak:

$ ../../ghc-whole-program-compiler-project/ghc-wpc/_build/stage1/bin/ghc -O2 tsumupto.hs
[1 of 1] Compiling Main ( tsumupto.hs, tsumupto.o )
ghc: could not execute: mkmodpak

Now that we have those files we can run the interpreter, but first though we need to make a *.fullpak file from the *.o_ghc_stgapp file and create a symbolic link to libHSbase-4.14.0.0.cbits.so:

## make the fullpack file
$ ~/.local/bin/mkfullpak tsumupto.o_ghc_stgapp
all modules: 259
app modules: 113
app dependencies:
... # bunch of output
...
main Main
creating tsumupto.fullpak

## create the link to the shared object file
$ ln -s ../../ghc-whole-program-compiler-project/external-stg-interpreter/data/cbits.so-script/libHSbase-4.14.0.0.cbits.so libHSbase-4.14.0.0.cbits.so

## the final directory should look like this
$ ls
libHSbase-4.14.0.0.cbits.so tsumupto tsumupto.fullpak tsumupto.hi tsumupto.hs tsumupto.o tsumupto.o_ghc_stgapp tsumupto.o_modpak

And now we can run the interpreter:

$ ~/.local/bin/ext-stg-interpreter tsumupto.fullpak
50005000
ssHeapStartAddress: 44082
ssTotalLNECount: 43
ssClosureCallCounter: 30275
executed closure id count: 112
call graph size: 146

The first line is the output of the program and the rest are diagnostics that the interpreter outputs. More importantly we should have a tab-separated csv file and call graph file in our local directory after running the interpreter:

$ ls -l
total 23876
lrwxrwxrwx 1 doyougnu users 114 Apr 30 12:21 libHSbase-4.14.0.0.cbits.so -> ../../ghc-whole-program-compiler-project/external-stg-interpreter/data/cbits.so-script/libHSbase-4.14.0.0.cbits.so
-rwxr-xr-x 1 doyougnu users 9442648 Apr 30 12:12 tsumupto
-rw-r--r-- 1 doyougnu users 53 Apr 30 12:23 tsumupto-call-graph-summary ### <---- interpreter output
-rw-r--r-- 1 doyougnu users 27490 Apr 30 12:23 tsumupto-call-graph.tsv ### <---- interpreter output
-rw------- 1 doyougnu users 14922366 Apr 30 12:19 tsumupto.fullpak
-rw-r--r-- 1 doyougnu users 1769 Apr 30 12:12 tsumupto.hi
-rw-r--r-- 1 doyougnu users 207 Apr 28 22:56 tsumupto.hs
-rw-r--r-- 1 doyougnu users 4488 Apr 30 12:12 tsumupto.o
-rw-r--r-- 1 doyougnu users 8817 Apr 30 12:12 tsumupto.o_ghc_stgapp
-rw------- 1 doyougnu users 9803 Apr 30 12:12 tsumupto.o_modpak

Which can be loaded into gephi for closer inspection of the call graph of our program. Be sure to watch the rest of the demo in Csabas talk for this part! For now well be going over using gephi and these files in our next blog post in this series, stay tuned!

Summary

File Descriptions

  • foo.modpak: A zip file which contains the Core, STG, CMM, source code, and assembly for the module foo
  • foo.fullpak: A zip file which contains the same information as modpack but for every module of the program rather than just module foo.
  • foo.o_ghc_stgapp: a yaml like file that contains:
    • the modules dependencies including package dependencies
    • a bunch of file paths for shared objects of the libraries
    • the flags the module was built with
  • libHSbase-4.14.0.0.cbits.so: shared object file created by ext-stg-interpreter/data/cbits.so-script.c. Required to be in the same directory as ext-stg-interpreter will be invoked.

Step-by-Step guide for running the interpreter on your code

  1. Build your project with ghc-wpc/_build/stage1/bin by directly invoking that ghc (as I did in the demo-01 project) or by pointing stack to it with system-ghc and extra-path in stack.yaml, or by passing -w <path-to-ghc-wpc-binary with cabal.
  2. Generate the foo.fullpak file with mkfullpak foo.o_ghc_stgapp
  3. Soft-link to libHSbase-4.14.0.0.cbits.so in the directory you will run the interpreter in. This file must be present when you run the interpreter!
  4. Now run the interpreter on project.fullpak
  5. Analyze foo-call-graph-summary and foo-call-graph.tsv with whatever tools make sense to you

Footnotes


  1. This isnt completely true, there is the RuntimeRep type controls exactly this and the levity polymorphism work by Richard Eisenberg. See this video for examples on using these features. We do plan to include a more thorough and real world example on using levity polymorphism for better performance in the haskell optimization handbook.