MLton 20180207

# ShareZeroVec

ShareZeroVec is an optimization pass for the SSA IntermediateLanguage, invoked from SSASimplify.

## Description

An SSA optimization to share zero-length vectors.

From be8c5f576, which replaced the use of the Array_array0Const primitive in the Basis Library implementation with a (nullary) Vector_vector primitive:

The original motivation for the Array_array0Const primitive was to share the heap space required for zero-length vectors among all vectors (of a given type). It was claimed that this optimization is important, e.g., in a self-compile, where vectors are used for lots of syntax tree elements and many of those vectors are empty. See: http://www.mlton.org/pipermail/mlton-devel/2002-February/021523.html

Curiously, the full effect of this optimization has been missing for quite some time (perhaps since the port of ConstantPropagation to the SSA IL). While ConstantPropagation has "globalized" the nullary application of the Array_array0Const primitive, it also simultaneously transformed it to an application of the Array_uninit (previously, the Array_array) primitive to the zero constant. The hash-consing of globals, meant to create exactly one global for each distinct constant, treats Array_uninit primitives as unequal (appropriately, since Array_uninit allocates an array with identity (though the identity may be supressed by a subsequent Array_toVector)), hence each distinct Array_array0Const primitive in the program remained as distinct globals. The limited amount of inlining prior to ConstantPropagation meant that there were typically fewer than a dozen "copies" of the same empty vector in a program for a given type.

As a "functional" primitive, a nullary Vector_vector is globalized by ClosureConvert, but is further recognized by ConstantPropagation and hash-consed into a unique instance for each type.

However, a single, shared, global Vector_vector () inhibits the coercion-based optimizations of Useless. For example, consider the following program:

        val n = valOf (Int.fromString (hd (CommandLine.arguments ())))

        val v1 = Vector.tabulate (n, fn i =>
                                  let val w = Word16.fromInt i
                                  in (w - 0wx1, w, w + 0wx1 + w)
                                  end)
        val v2 = Vector.map (fn (w1, w2, w3) => (w1, 0wx2 * w2, 0wx3 * w3)) v1
        val v3 = VectorSlice.vector (VectorSlice.slice (v1, 1, SOME (n - 2)))
        val ans1 = Vector.foldl (fn ((w1,w2,w3),w) => w + w1 + w2 + w3) 0wx0 v1
        val ans2 = Vector.foldl (fn ((_,w2,_),w) => w + w2) 0wx0 v2
        val ans3 = Vector.foldl (fn ((_,w2,_),w) => w + w2) 0wx0 v3

        val _ = print (concat ["ans1 = ", Word16.toString ans1, "  ",
                               "ans2 = ", Word16.toString ans2, "  ",
                               "ans3 = ", Word16.toString ans3, "\n"])

We would like v2 and v3 to be optimized from (word16 * word16 * word16) vector to word16 vector because only the 2nd component of the elements is needed to compute the answer.

With Array_array0Const, each distinct occurrence of Array_array0Const((word16 * word16 * word16)) arising from polyvariance and inlining remained a distinct Array_uninit((word16 * word16 * word16)) (0x0) global, which resulted in distinct occurrences for the val v1 = Vector.tabulate ... and for the val v2 = Vector.map .... The latter could be optimized to Array_uninit(word16) (0x0) by Useless, because its result only flows to places requiring the 2nd component of the elements.

With Vector_vector (), the distinct occurrences of Vector_vector((word16 * word16 * word16)) () arising from polyvariance are globalized during ClosureConvert, those global references may be further duplicated by inlining, but the distinct occurrences of Vector_vector((word16 * word16 * word16)) () are merged to a single occurrence. Because this result flows to places requiring all three components of the elements, it remains Vector_vector((word16 * word16 * word16)) () after Useless. Furthermore, because one cannot (in constant time) coerce a (word16 * word16 * word16) vector to a word16 vector, the v2 value remains of type (word16 * word16 * word16) vector.

One option would be to drop the 0-element vector "optimization" entirely. This costs some space (no sharing of empty vectors) and some time (allocation and garbage collection of empty vectors).

Another option would be to reinstate the Array_array0Const primitive and associated ConstantPropagation treatment. But, the semantics and purpose of Array_array0Const was poorly understood, resulting in this break.

The ShareZeroVec pass pursues a different approach: perform the 0-element vector "optimization" as a separate optimization, after ConstantPropagation and Useless. A trivial static analysis is used to match val v: t vector = Array_toVector(t) (a) with corresponding val a: array = Array_uninit(t) (l) and the later are expanded to val a: t array = if 0 = l then zeroArr_[t] else Array_uninit(t) (l) with a single global val zeroArr_[t] = Array_uninit(t) (0) created for each distinct type (after coercion-based optimizations).

One disadvantage of this approach, compared to the Vector_vector(t) () approach, is that Array_toVector is applied each time a vector is created, even if it is being applied to the zeroArr_[t] zero-length array. (Although, this was the behavior of the Array_array0Const approach.) This updates the object header each time, whereas the Vector_vector(t) () approach would have updated the object header once, when the global was created, and the zeroVec_[t] global and the Array_toVector result would flow to the join point.

It would be possible to properly share zero-length vectors, but doing so is a more sophisticated analysis and transformation, because there can be arbitrary code between the val a: t array = Array_uninit(t) (l) and the corresponding val v: v vector = Array_toVector(t) (a), although, in practice, nothing happens when a zero-length vector is created. It may be best to pursue a more general "array to vector" optimization that transforms creations of static-length vectors (e.g., all the Vector.new<N> functions) into Vector_vector primitives (some of which could be globalized).

## Implementation

- share-zero-vec.fun

## Details and Notes