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8272861: Add a micro benchmark for vector api
Reviewed-by: psandoz
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Sandhya Viswanathan committed Aug 30, 2021
1 parent 7a01ba6 commit 5aaa20f898e8679ef1c2c36bd01d48c17be0aacf
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  1. +189 −0 test/micro/org/openjdk/bench/jdk/incubator/vector/BlackScholes.java
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/*
* 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);
}
}
}

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