middle-end: Add new parameter to scale scalar loop costing in vectorizer

This patch adds a new param vect-scalar-cost-multiplier to scale the scalar
costing during vectorization.  If the cost is set high enough and when using
the dynamic cost model it has the effect of effectively disabling the
costing vs scalar and assumes all vectorization to be profitable.

This is similar to using the unlimited cost model, but unlike unlimited it
does not fully disable the vector cost model.  That means that we still
perform comparisons between vector modes.  And it means it also still does
costing for alias analysis.

As an example, the following:

void
foo (char *restrict a, int *restrict b, int *restrict c,
     int *restrict d, int stride)
{
    if (stride <= 1)
        return;

    for (int i = 0; i < 3; i++)
        {
            int res = c[i];
            int t = b[i * stride];
            if (a[i] != 0)
                res = t * d[i];
            c[i] = res;
        }
}

compiled with -O3 -march=armv8-a+sve -fvect-cost-model=dynamic fails to
vectorize as it assumes scalar would be faster, and with
-fvect-cost-model=unlimited it picks a vector type that's so big that the large
sequence generated is working on mostly inactive lanes:

        ...
        and     p3.b, p3/z, p4.b, p4.b
        whilelo p0.s, wzr, w7
        ld1w    z23.s, p3/z, [x3, #3, mul vl]
        ld1w    z28.s, p0/z, [x5, z31.s, sxtw 2]
        add     x0, x5, x0
        punpklo p6.h, p6.b
        ld1w    z27.s, p4/z, [x0, z31.s, sxtw 2]
        and     p6.b, p6/z, p0.b, p0.b
        punpklo p4.h, p7.b
        ld1w    z24.s, p6/z, [x3, #2, mul vl]
        and     p4.b, p4/z, p2.b, p2.b
        uqdecw  w6
        ld1w    z26.s, p4/z, [x3]
        whilelo p1.s, wzr, w6
        mul     z27.s, p5/m, z27.s, z23.s
        ld1w    z29.s, p1/z, [x4, z31.s, sxtw 2]
        punpkhi p7.h, p7.b
        mul     z24.s, p5/m, z24.s, z28.s
        and     p7.b, p7/z, p1.b, p1.b
        mul     z26.s, p5/m, z26.s, z30.s
        ld1w    z25.s, p7/z, [x3, #1, mul vl]
        st1w    z27.s, p3, [x2, #3, mul vl]
        mul     z25.s, p5/m, z25.s, z29.s
        st1w    z24.s, p6, [x2, #2, mul vl]
        st1w    z25.s, p7, [x2, #1, mul vl]
        st1w    z26.s, p4, [x2]
        ...

With -fvect-cost-model=dynamic --param vect-scalar-cost-multiplier=200
you get more reasonable code:

foo:
        cmp     w4, 1
        ble     .L1
        ptrue   p7.s, vl3
        index   z0.s, #0, w4
        ld1b    z29.s, p7/z, [x0]
        ld1w    z30.s, p7/z, [x1, z0.s, sxtw 2]
	ptrue   p6.b, all
        cmpne   p7.b, p7/z, z29.b, #0
        ld1w    z31.s, p7/z, [x3]
	mul     z31.s, p6/m, z31.s, z30.s
        st1w    z31.s, p7, [x2]
.L1:
        ret

This model has been useful internally for performance exploration and cost-model
validation.  It allows us to force realistic vectorization overriding the cost
model to be able to tell whether it's correct wrt to profitability.

gcc/ChangeLog:

	* params.opt (vect-scalar-cost-multiplier): New.
	* tree-vect-loop.cc (vect_estimate_min_profitable_iters): Use it.
	* doc/invoke.texi (vect-scalar-cost-multiplier): Document it.

gcc/testsuite/ChangeLog:

	* gcc.target/aarch64/sve/cost_model_16.c: New test.

diff --git a/gcc/doc/invoke.texi b/gcc/doc/invoke.texi
index 189a52b9b9590c18188208e612a09f17383147c3..17929b3cf15031574b7ec22d9866db493669e35d 100644 (file)
--- a/gcc/doc/invoke.texi
+++ b/gcc/doc/invoke.texi
@@ -17325,6 +17325,10 @@ this parameter.  The default value of this parameter is 50.
 @item vect-induction-float
 Enable loop vectorization of floating point inductions.
 
+@item vect-scalar-cost-multiplier
+Apply the given multiplier % to scalar loop costing during vectorization.
+Increasing the cost multiplier will make vector loops more profitable.
+
 @item vrp-block-limit
 Maximum number of basic blocks before VRP switches to a lower memory algorithm.
 

diff --git a/gcc/params.opt b/gcc/params.opt
index 1f0abeccc4b9b439ad4a4add6257b4e50962863d..a67f900a63f7187b1daa593fe17cd88f2fc32367 100644 (file)
--- a/gcc/params.opt
+++ b/gcc/params.opt
@@ -1253,6 +1253,10 @@ The maximum factor which the loop vectorizer applies to the cost of statements i
 Common Joined UInteger Var(param_vect_induction_float) Init(1) IntegerRange(0, 1) Param Optimization
 Enable loop vectorization of floating point inductions.
 
+-param=vect-scalar-cost-multiplier=
+Common Joined UInteger Var(param_vect_scalar_cost_multiplier) Init(100) IntegerRange(0, 10000) Param Optimization
+The scaling multiplier as a percentage to apply to all scalar loop costing when performing vectorization profitability analysis.  The default value is 100.
+
 -param=vrp-block-limit=
 Common Joined UInteger Var(param_vrp_block_limit) Init(150000) Optimization Param
 Maximum number of basic blocks before VRP switches to a fast model with less memory requirements.

diff --git a/gcc/testsuite/gcc.target/aarch64/sve/cost_model_16.c b/gcc/testsuite/gcc.target/aarch64/sve/cost_model_16.c
new file mode 100644 (file)
index 0000000..bfe49ef
--- /dev/null
+++ b/gcc/testsuite/gcc.target/aarch64/sve/cost_model_16.c
@@ -0,0 +1,21 @@
+/* { dg-do compile } */
+/* { dg-options "-Ofast -march=armv8-a+sve --param vect-scalar-cost-multiplier=1000 -fdump-tree-vect-details" } */
+
+void
+foo (char *restrict a, int *restrict b, int *restrict c,
+     int *restrict d, int stride)
+{
+    if (stride <= 1)
+        return;
+
+    for (int i = 0; i < 3; i++)
+        {
+            int res = c[i];
+            int t = b[i * stride];
+            if (a[i] != 0)
+                res = t * d[i];
+            c[i] = res;
+        }
+}
+
+/* { dg-final { scan-tree-dump "vectorized 1 loops in function" "vect" } } */

diff --git a/gcc/tree-vect-loop.cc b/gcc/tree-vect-loop.cc
index 8c5761d3c55d52c95829f98e4215ae80cebe85d0..9ac4d7e5f7a099a7039cd4186666cf64328b8ee6 100644 (file)
--- a/gcc/tree-vect-loop.cc
+++ b/gcc/tree-vect-loop.cc
@@ -4646,7 +4646,8 @@ vect_estimate_min_profitable_iters (loop_vec_info loop_vinfo,
      TODO: Consider assigning different costs to different scalar
      statements.  */
 
-  scalar_single_iter_cost = loop_vinfo->scalar_costs->total_cost ();
+  scalar_single_iter_cost = (loop_vinfo->scalar_costs->total_cost ()
+                            * param_vect_scalar_cost_multiplier) / 100;
 
   /* Add additional cost for the peeled instructions in prologue and epilogue
      loop.  (For fully-masked loops there will be no peeling.)