Introduction​

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

In the context of a program produced by the GHC JavaScript backend, two different types of stack exist: The JavaScript call stack and Haskell lightweight stacks. In this post we will focus mostly on the lightweight stacks.

First we will see why using only the JavaScript call stack is not suitable for running compiled Haskell code. Then we will introduce the calling convention we use for Haskell and see how the lightweight stacks are used for making calls and passing around data. After this, we will explore in more detail how they are used for exception handling and multithreading.

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.

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.

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...

1. The Design Space
2. GHCJS’s 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 backend’s 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 user’s program against for the JS backend to optimize the imported function and for good error messages for users.

GHCJS’s FFI sets a high (qualitative) benchmark on these three constraints. Let’s inspect them each in detail, in no particular order.

GHCJS’s FFI

In GHCJS, a user could take advantage of JavaScript functions in their Haskell code using the GHCJS’s 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 GHCJS’s 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 GHCJS’s 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 GHC’s 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 we’ll be overhauling the FFI system and returning it to its former glory.

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:

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:

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:

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.

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#. We’ll 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. We’ll expand on the use of objects—especially representing heap objects—in 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 doesn’t 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. We’ll 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.

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.

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:

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:

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.