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Primitive Type Representation in GHC's upcoming JS-backend

ยท 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#. Weโ€™ll begin with the easy cases.

The Easy Casesโ€‹

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. Weโ€™ll expand on the use of objectsโ€”especially representing heap objectsโ€”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

// 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 = ++h$mvarId;

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 doesnโ€™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#,โ€‹

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#โ€‹

Addr# and StablePtr# are implemented as a pair of ByteArray# and an Int# offset into the array. Weโ€™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 Caseโ€‹

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 Typesโ€‹

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 Optimizationโ€‹

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.


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


  • 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!โ€‹

  • 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.โ†ฉ