Add masked filter-and-sum kernel

timings/src/main/java/com/ankurm/vectorapi/MaskedFilter.java

@@ -0,0 +1,45 @@
+package com.ankurm.vectorapi;
+
+import jdk.incubator.vector.*;
+
+/** Sum only the elements above a threshold -- a data-dependent branch. */
+public final class MaskedFilter {
+
+    static final float THRESH = 0.5f;
+    static final VectorSpecies<Float> SP = FloatVector.SPECIES_PREFERRED;
+
+    // Scalar: the branch is data-dependent, so C2 generally cannot vectorize this form
+    static float scalar(float[] a) {
+        float sum = 0;
+        for (int i = 0; i < a.length; i++)
+            if (a[i] > THRESH) sum += a[i];
+        return sum;
+    }
+
+    static float vector(float[] a) {
+        FloatVector acc = FloatVector.zero(SP);
+        int i = 0, bound = SP.loopBound(a.length);
+        for (; i < bound; i += SP.length()) {
+            var v = FloatVector.fromArray(SP, a, i);
+            VectorMask<Float> m = v.compare(VectorOperators.GT, THRESH);
+            acc = acc.add(v, m);                        // add only where mask is true
+        }
+        float sum = acc.reduceLanes(VectorOperators.ADD);   // horizontal sum of the lanes
+        for (; i < a.length; i++) if (a[i] > THRESH) sum += a[i];   // masked-off tail
+        return sum;
+    }
+
+    static void run(int n) {
+        float[] a = Data.randomFloats(n, 3);
+        float s0 = scalar(a), v0 = vector(a);
+
+        System.out.println("--- kernel 2: masked filter-and-sum, " + n + " floats ---");
+        System.out.printf("scalar sum = %.4f%nvector sum = %.4f  (same value, different rounding)%n", s0, v0);
+        double s = Bench.measure("scalar (branch)", () -> Sink.consume(scalar(a)));
+        double v = Bench.measure("Vector API (masked)", () -> Sink.consume(vector(a)));
+        Bench.speedup("vector advantage", s, v);
+        System.out.println();
+    }
+
+    private MaskedFilter() {}
+}