8272861: Add a micro benchmark for vector api

Reviewed-by: psandoz

Author: Sandhya Viswanathan

test/micro/org/openjdk/bench/jdk/incubator/vector/BlackScholes.java

@@ -0,0 +1,189 @@
+/*
+ *  Copyright (c) 2021, Intel Corporation. All rights reserved.
+ *  DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+ *
+ *  This code is free software; you can redistribute it and/or modify it
+ *  under the terms of the GNU General Public License version 2 only, as
+ *  published by the Free Software Foundation.
+ *
+ *  This code is distributed in the hope that it will be useful, but WITHOUT
+ *  ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+ *  FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
+ *  version 2 for more details (a copy is included in the LICENSE file that
+ *  accompanied this code).
+ *
+ *  You should have received a copy of the GNU General Public License version
+ *  2 along with this work; if not, write to the Free Software Foundation,
+ *  Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
+ *
+ *  Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
+ *  or visit www.oracle.com if you need additional information or have any
+ *  questions.
+ *
+ */
+
+package org.openjdk.bench.jdk.incubator.vector;
+
+import jdk.incubator.vector.FloatVector;
+import jdk.incubator.vector.VectorOperators;
+import jdk.incubator.vector.VectorSpecies;
+import org.openjdk.jmh.annotations.*;
+
+import java.util.Objects;
+import java.util.Random;
+import java.util.concurrent.TimeUnit;
+import java.util.function.IntUnaryOperator;
+
+@BenchmarkMode(Mode.Throughput)
+@OutputTimeUnit(TimeUnit.SECONDS)
+@State(Scope.Thread)
+@Warmup(iterations = 3, time = 5)
+@Measurement(iterations = 3, time = 5)
+@Fork(value = 1, jvmArgsPrepend = {"--add-modules=jdk.incubator.vector"})
+public class BlackScholes {
+
+    @Param("1024")
+    int size;
+
+
+    float[] s0; // Stock Price
+    float[] x;  // Strike Price
+    float[] t;  // Maturity
+    float[] call;
+    float[] put;
+    float r;    // risk-neutrality
+    float sig;  // volatility
+    Random rand;
+
+
+    float randFloat(float low, float high) {
+       float val = rand.nextFloat();
+       return (1.0f - val) * low + val * high;
+    }
+
+    float[] fillRandom(float low, float high) {
+        float[] array = new float[size];
+        for (int i = 0; i < array.length; i++) {
+            array[i] = randFloat(low, high);
+        }
+        return array;
+    }
+
+    @Setup
+    public void init() {
+        rand = new Random();
+        s0 = fillRandom(5.0f, 30.0f);
+        x  = fillRandom(1.0f, 100.0f);
+        t  = fillRandom(0.25f, 10.0f);
+        r = 0.02f;
+        sig = 0.30f;
+        call = new float[size];
+        put = new float[size];
+    }
+
+    static final float Y = 0.2316419f;
+    static final float A1 = 0.31938153f;
+    static final float A2 = -0.356563782f;
+    static final float A3 = 1.781477937f;
+    static final float A4 = -1.821255978f;
+    static final float A5 = 1.330274429f;
+    static final float PI = (float)Math.PI;
+
+    float cdf(float inp) {
+        float x = inp;
+        if (inp < 0f) {
+            x = -inp;
+        }
+
+        float term = 1f / (1f + (Y * x));
+        float term_pow2 = term * term;
+        float term_pow3 = term_pow2 * term;
+        float term_pow4 = term_pow2 * term_pow2;
+        float term_pow5 = term_pow2 * term_pow3;
+
+        float part1 = (1f / (float)Math.sqrt(2f * PI)) * (float)Math.exp((-x * x) * 0.5f);
+
+        float part2 = (A1 * term) +
+                      (A2 * term_pow2) +
+                      (A3 * term_pow3) +
+                      (A4 * term_pow4) +
+                      (A5 * term_pow5);
+
+        if (inp >= 0f)
+            return 1f - part1 * part2;
+        else
+            return part1 * part2;
+
+    }
+
+    public void scalar_black_scholes_kernel(int off) {
+        float sig_sq_by2 = 0.5f * sig * sig;
+        for (int i = off; i < size; i++ ) {
+            float log_s0byx = (float)Math.log(s0[i] / x[i]);
+            float sig_sqrt_t = sig * (float)Math.sqrt(t[i]);
+            float exp_neg_rt = (float)Math.exp(-r * t[i]);
+            float d1 = (log_s0byx + (r + sig_sq_by2) * t[i])/(sig_sqrt_t);
+            float d2 = d1 - sig_sqrt_t;
+            call[i] = s0[i] * cdf(d1) - exp_neg_rt * x[i] * cdf(d2);
+            put[i]  = call[i] + exp_neg_rt - s0[i];
+       }
+    }
+
+    @Benchmark
+    public void scalar_black_scholes() {
+        scalar_black_scholes_kernel(0);
+    }
+
+    static final VectorSpecies<Float> fsp = FloatVector.SPECIES_PREFERRED;
+
+    FloatVector vcdf(FloatVector vinp) {
+        var vx = vinp.abs();
+        var vone = FloatVector.broadcast(fsp, 1.0f);
+        var vtwo = FloatVector.broadcast(fsp, 2.0f);
+        var vterm = vone.div(vone.add(vx.mul(Y)));
+        var vterm_pow2 = vterm.mul(vterm);
+        var vterm_pow3 = vterm_pow2.mul(vterm);
+        var vterm_pow4 = vterm_pow2.mul(vterm_pow2);
+        var vterm_pow5 = vterm_pow2.mul(vterm_pow3);
+        var vpart1 = vone.div(vtwo.mul(PI).lanewise(VectorOperators.SQRT)).mul(vx.mul(vx).neg().lanewise(VectorOperators.EXP).mul(0.5f));
+        var vpart2 = vterm.mul(A1).add(vterm_pow2.mul(A2)).add(vterm_pow3.mul(A3)).add(vterm_pow4.mul(A4)).add(vterm_pow5.mul(A5));
+        var vmask = vinp.compare(VectorOperators.GT, 0f);
+        var vresult1 = vpart1.mul(vpart2);
+        var vresult2 = vresult1.neg().add(vone);
+        var vresult = vresult1.blend(vresult2, vmask);
+
+        return vresult;
+    }
+
+    public int vector_black_scholes_kernel() {
+        int i = 0;
+        var vsig = FloatVector.broadcast(fsp, sig);
+        var vsig_sq_by2 = vsig.mul(vsig).mul(0.5f);
+        var vr = FloatVector.broadcast(fsp, r);
+        var vnegr = FloatVector.broadcast(fsp, -r);
+        for (; i <= x.length - fsp.length(); i += fsp.length()) {
+            var vx = FloatVector.fromArray(fsp, x, i);
+            var vs0 = FloatVector.fromArray(fsp, s0, i);
+            var vt = FloatVector.fromArray(fsp, t, i);
+            var vlog_s0byx = vs0.div(vx).lanewise(VectorOperators.LOG);
+            var vsig_sqrt_t = vt.lanewise(VectorOperators.SQRT).mul(vsig);
+            var vexp_neg_rt = vt.mul(vnegr).lanewise(VectorOperators.EXP);
+            var vd1 = vsig_sq_by2.add(vr).mul(vt).add(vlog_s0byx).div(vsig_sqrt_t);
+            var vd2 = vd1.sub(vsig_sqrt_t);
+            var vcall = vs0.mul(vcdf(vd1)).sub(vx.mul(vexp_neg_rt).mul(vcdf(vd2)));
+            var vput = vcall.add(vexp_neg_rt).sub(vs0);
+            vcall.intoArray(call, i);
+            vput.intoArray(put, i);
+        }
+        return i;
+    }
+
+    @Benchmark
+    public void vector_black_scholes() {
+        int processed = vector_black_scholes_kernel();
+        if (processed < size) {
+            scalar_black_scholes_kernel(processed);
+        }
+    }
+}
+