[vector] RFR (L): Generalized intrinsics for vector operations (first batch)

[Paul Sandoz]

Looks good.

 139     @Override
 140     @ForceInline
 141     public $vectortype$ add(Vector<$Boxtype$,Shapes.$shape$> v) {
 142         Objects.requireNonNull(v);
 143         return ($vectortype$) VectorIntrinsics.binaryOp(
 144             VECTOR_OP_ADD, $vectortype$.class, $type$.class, LENGTH,
 145             this, ($vectortype$)v,
 146             (v1, v2) -> (($vectortype$)v1).bOp(v2, (i, a, b) -> ($type$)(a + b)));
 147     }

Given you perform the cast at line 145, so both input vectors are of the same concrete type, do you require it on v1 in the lambda?

Paul.

> On Feb 9, 2018, at 1:38 PM, Vladimir Ivanov <vladimir.x.ivanov at oracle.com> wrote:
> 
> http://cr.openjdk.java.net/~vlivanov/panama/vector.generalized_intrinsics/webrev.06/
> 
> Here's the first batch of rewritten intrinsics for vector operations.
> 
> Main motivation for new implementation is:
>  * reduce number of intrinsic needed;
>  * improve intrinsification robustness;
>  * minimize changes needed in shared C2 code.
> 
> The idea of generalized intrinsics is to parameterize them with additional information (passed as constant arguments), so JIT-compiler has enough information to dispatch to proper implementation during intrinsification.
> 
> For example, binary vector operation can be represented as:
> 
>  // (V,V) -> V
>  @HotSpotIntrinsicCandidate
>  static <V> V binaryOp(int oprId, Class<V> vectorClass,
>                        Class<?> elementType, int vlen,
>                        V v1, V v2,
>                        BiFunction<V,V,V> defaultImpl) {
>      return defaultImpl.apply(v1, v2);
>  }
> 
> and used as:
> 
>  // (Int256Vector,Int256Vector) -> Int256Vector
> 
>  public Int256Vector add(Vector<Integer,Shapes.S256Bit> v) {
>    return (Int256Vector) VectorIntrinsics.binaryOp(
>            VECTOR_OP_ADD,
>            Int256Vector.class, int.class, 8,
>            this, (Int256Vector)v,
>            (v1, v2) -> ((Int256Vector)v1).bOp(v2,
>                                   (i, a, b) -> (int)(a+b)));
>  }
> 
> where:
>  oprId encodes actual operation (VECTOR_OP_ADD);
> 
>  vectorClass, elementType, vlen describe concrete vector class (Int256Vector);
> 
>  v1, v2 are actual arguments of vector binary operations;
> 
>  defaultImpl - scalar implementation which is used when intrinsification fails.
> 
> 
> Generalized intrinsics are declared on jdk.incubator.vector.VectorIntrinsics and the patch contains 6 of them:
>  * broadcastCoerced
>  * reductionCoerced
>  * binaryOp
>  * load/store
>  * test
> 
> The following vector operations were ported to new mechanism:
>  * broadcast: zero, broadcast, trueMask, falseMask
>  * reduction: addAll, mulAll
>  * binaryOp: add, sub, mul, div, and, or, xor
>  * load/store: intoArray, fromArray
>  * test: anyTrue, allTrue
> 
> There's new flag added to turn new intrinsics on/off:
> 
> +  product(bool, UseVectorApiGeneralizedIntrinsics, true,
> 
> Previous discussions [1] [2].
> 
> The patch adds alternative implementations, but doesn't remove existing ones, since there are some code dependencies on them. Once the dependencies are broken, the code will go away.
> 
> Thanks!
> 
> Best regards,
> Vladimir Ivanov
> 
> [1] http://mail.openjdk.java.net/pipermail/panama-dev/2017-November/000748.html
> 
> [2] http://mail.openjdk.java.net/pipermail/panama-dev/2017-December/000884.html