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/*
* (c) Copyright Christian P. Fries, Germany. All rights reserved. Contact: [email protected].
*
* Created on 09.02.2006
*/
package net.finmath.montecarlo.cuda.alternative;
import static jcuda.driver.JCudaDriver.cuCtxCreate;
import static jcuda.driver.JCudaDriver.cuCtxSynchronize;
import static jcuda.driver.JCudaDriver.cuDeviceGet;
import static jcuda.driver.JCudaDriver.cuInit;
import static jcuda.driver.JCudaDriver.cuLaunchKernel;
import static jcuda.driver.JCudaDriver.cuModuleGetFunction;
import static jcuda.driver.JCudaDriver.cuModuleLoad;
import java.io.ByteArrayOutputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.lang.ref.ReferenceQueue;
import java.lang.ref.WeakReference;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.function.DoubleBinaryOperator;
import java.util.function.DoubleUnaryOperator;
import java.util.function.IntToDoubleFunction;
import java.util.logging.Logger;
import java.util.stream.DoubleStream;
import jcuda.LogLevel;
import jcuda.Pointer;
import jcuda.Sizeof;
import jcuda.driver.CUcontext;
import jcuda.driver.CUdevice;
import jcuda.driver.CUdeviceptr;
import jcuda.driver.CUfunction;
import jcuda.driver.CUmodule;
import jcuda.driver.JCudaDriver;
import net.finmath.functions.DoubleTernaryOperator;
import net.finmath.stochastic.RandomVariable;
/**
* The class RandomVariableFromDoubleArray represents a random variable being the evaluation of a stochastic process
* at a certain time within a Monte-Carlo simulation.
* It is thus essentially a vector of floating point numbers - the realizations - together with a double - the time.
* The index of the vector represents path.
* The class may also be used for non-stochastic quantities which may potentially be stochastic
* (e.g. volatility). If only non-stochastic random variables are involved in an operation the class uses
* optimized code.
*
* Accesses performed exclusively through the interface
* RandomVariable is thread safe (and does not mutate the class).
*
* This implementation uses floats for the realizations (consuming less memory compared to using doubles). However,
* the calculation of the average is performed using double precision.
*
* @author Christian Fries
* @version 1.8
*/
public class RandomVariableCudaWithFinalizer implements RandomVariable {
private static final long serialVersionUID = 7620120320663270600L;
private final double time; // Time (filtration)
// Data model for the stochastic case (otherwise null)
private final CUdeviceptr realizations; // Realizations
private final long size;
// Data model for the non-stochastic case (if realizations==null)
private final double valueIfNonStochastic;
private final static ReferenceQueue referenceQueue = new ReferenceQueue();
private final static Map, CUdeviceptr> referenceMap = new ConcurrentHashMap, CUdeviceptr>();
private final static Logger logger = Logger.getLogger("net.finmath");
public final static CUdevice device;
public final static CUcontext context;
private final static CUfunction capByScalar;
private final static CUfunction floorByScalar;
private final static CUfunction addScalar;
private final static CUfunction subScalar;
private final static CUfunction multScalar;
private final static CUfunction divScalar;
private final static CUfunction cuPow;
private final static CUfunction cuSqrt;
private final static CUfunction cuExp;
private final static CUfunction cuLog;
private final static CUfunction invert;
private final static CUfunction cuAbs;
private final static CUfunction cap;
private final static CUfunction cuFloor;
private final static CUfunction add;
private final static CUfunction sub;
private final static CUfunction mult;
private final static CUfunction cuDiv;
private final static CUfunction accrue;
private final static CUfunction discount;
private final static CUfunction reducePartial;
private final static int reduceGridSize = 1024;
// Initalize cuda
static {
// Enable exceptions and omit all subsequent error checks
JCudaDriver.setExceptionsEnabled(true);
JCudaDriver.setLogLevel(LogLevel.LOG_DEBUG);
// Create the PTX file by calling the NVCC
String ptxFileName = null;
try {
ptxFileName = preparePtxFile("RandomVariableCudaKernel.cu");
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// Initialize the driver and create a context for the first device.
cuInit(0);
device = new CUdevice();
cuDeviceGet(device, 0);
context = new CUcontext();
cuCtxCreate(context, 0, device);
// Load the ptx file.
CUmodule module = new CUmodule();
cuModuleLoad(module, ptxFileName);
// Obtain a function pointers
capByScalar = new CUfunction();
cuModuleGetFunction(capByScalar, module, "capByScalar");
floorByScalar = new CUfunction();
cuModuleGetFunction(floorByScalar, module, "floorByScalar");
addScalar = new CUfunction();
cuModuleGetFunction(addScalar, module, "addScalar");
subScalar = new CUfunction();
cuModuleGetFunction(subScalar, module, "subScalar");
multScalar = new CUfunction();
cuModuleGetFunction(multScalar, module, "multScalar");
divScalar = new CUfunction();
cuModuleGetFunction(divScalar, module, "divScalar");
cuPow = new CUfunction();
cuModuleGetFunction(cuPow, module, "cuPow");
cuSqrt = new CUfunction();
cuModuleGetFunction(cuSqrt, module, "cuSqrt");
cuExp = new CUfunction();
cuModuleGetFunction(cuExp, module, "cuExp");
cuLog = new CUfunction();
cuModuleGetFunction(cuLog, module, "cuLog");
invert = new CUfunction();
cuModuleGetFunction(invert, module, "invert");
cuAbs = new CUfunction();
cuModuleGetFunction(cuAbs, module, "cuAbs");
cap = new CUfunction();
cuModuleGetFunction(cap, module, "cap");
cuFloor = new CUfunction();
cuModuleGetFunction(cuFloor, module, "cuFloor");
add = new CUfunction();
cuModuleGetFunction(add, module, "add");
sub = new CUfunction();
cuModuleGetFunction(sub, module, "sub");
mult = new CUfunction();
cuModuleGetFunction(mult, module, "mult");
cuDiv = new CUfunction();
cuModuleGetFunction(cuDiv, module, "cuDiv");
accrue = new CUfunction();
cuModuleGetFunction(accrue, module, "accrue");
discount = new CUfunction();
cuModuleGetFunction(accrue, module, "discount");
reducePartial = new CUfunction();
cuModuleGetFunction(reducePartial, module, "reducePartial");
}
public RandomVariableCudaWithFinalizer(double time, CUdeviceptr realizations, long size) {
this.time = time;
this.realizations = realizations;
this.size = size;
this.valueIfNonStochastic = Double.NaN;
// Manage CUdeviceptr
WeakReference reference = new WeakReference(this, referenceQueue);
referenceMap.put(reference, realizations);
}
/**
* Create a non stochastic random variable, i.e. a constant.
*
* @param value the value, a constant.
*/
public RandomVariableCudaWithFinalizer(double value) {
this(-Double.MAX_VALUE, value);
}
/**
* Create a non stochastic random variable, i.e. a constant.
*
* @param time the filtration time, set to 0.0 if not used.
* @param value the value, a constant.
*/
public RandomVariableCudaWithFinalizer(double time, double value) {
super();
this.time = time;
this.realizations = null;
this.size = 1;
this.valueIfNonStochastic = value;
}
/**
* Create a stochastic random variable.
*
* @param time the filtration time, set to 0.0 if not used.
* @param realisations the vector of realizations.
*/
public RandomVariableCudaWithFinalizer(double time, float[] realisations) {
super();
this.time = time;
this.size = realisations.length;
this.realizations = createCUdeviceptr(realisations);
this.valueIfNonStochastic = Double.NaN;
}
/**
* Create a stochastic random variable.
*
* @param time the filtration time, set to 0.0 if not used.
* @param realisations the vector of realizations.
*/
public RandomVariableCudaWithFinalizer(double time, double[] realisations) {
this(time, getFloatArray(realisations));
}
private CUdeviceptr createCUdeviceptr(long size) {
CUdeviceptr cuDevicePtr = getCUdeviceptr(size);
return cuDevicePtr;
}
public static CUdeviceptr getCUdeviceptr(long size) {
CUdeviceptr cuDevicePtr = new CUdeviceptr();
int succ = JCudaDriver.cuMemAlloc(cuDevicePtr, size * Sizeof.FLOAT);
if(succ != 0) {
cuDevicePtr = null;
logger.finest("Failed creating device vector "+ cuDevicePtr + " with size=" + size);
}
else {
logger.finest("Creating device vector "+ cuDevicePtr + " with size=" + size);
}
return cuDevicePtr;
}
/**
* Create a vector on device and copy host vector to it.
*
* @param values Host vector.
* @return Pointer to device vector.
*/
private CUdeviceptr createCUdeviceptr(float[] values) {
CUdeviceptr cuDevicePtr = createCUdeviceptr((long)values.length);
JCudaDriver.cuMemcpyHtoD(cuDevicePtr, Pointer.to(values),
(long)values.length * Sizeof.FLOAT);
return cuDevicePtr;
}
@Override
protected void finalize() throws Throwable {
System.out.println("Finalizing " + realizations);
if(realizations != null) {
JCudaDriver.cuMemFree(realizations);
}
super.finalize();
}
private static float[] getFloatArray(double[] arrayOfDouble) {
float[] arrayOfFloat = new float[arrayOfDouble.length];
for(int i=0; i quantileEnd) return getQuantileExpectation(quantileEnd, quantileStart);
throw new UnsupportedOperationException();
/*
float[] realizationsSorted = realizations.clone();
java.util.Arrays.sort(realizationsSorted);
int indexOfQuantileValueStart = Math.min(Math.max((int)Math.round((size()+1) * quantileStart - 1), 0), size()-1);
int indexOfQuantileValueEnd = Math.min(Math.max((int)Math.round((size()+1) * quantileEnd - 1), 0), size()-1);
double quantileExpectation = 0.0;
for (int i=indexOfQuantileValueStart; i<=indexOfQuantileValueEnd;i++) {
quantileExpectation += realizationsSorted[i];
}
quantileExpectation /= indexOfQuantileValueEnd-indexOfQuantileValueStart+1;
return quantileExpectation;
*/
}
@Override
public double[] getHistogram(double[] intervalPoints)
{
throw new UnsupportedOperationException();
/*
double[] histogramValues = new double[intervalPoints.length+1];
if(isDeterministic()) {
java.util.Arrays.fill(histogramValues, 0.0);
for (int intervalIndex=0; intervalIndex intervalPoints[intervalIndex]) {
histogramValues[intervalIndex] = 1.0;
break;
}
}
histogramValues[intervalPoints.length] = 1.0;
}
else {
float[] realizationsSorted = realizations.clone();
java.util.Arrays.sort(realizationsSorted);
int sampleIndex=0;
for (int intervalIndex=0; intervalIndex 0) {
for(int i=0; i 1) {
reduced = reduced.reduceBySize(reduceGridSize);
}
return reduced.getRealizations()[0];
}
private RandomVariableCudaWithFinalizer reduceBySize(int bySize) {
int blockSizeX = bySize;
int gridSizeX = (int)Math.ceil((double)size()/2 / blockSizeX);
CUdeviceptr reduceVector = getCUdeviceptr(gridSizeX);
callCudaFunction(reducePartial, new Pointer[] {
Pointer.to(new int[] { size() }),
Pointer.to(realizations),
Pointer.to(reduceVector)},
gridSizeX, blockSizeX, blockSizeX);
return new RandomVariableCudaWithFinalizer(0.0, reduceVector, gridSizeX);
}
private CUdeviceptr callCudaFunction(CUfunction function, Pointer[] arguments) {
// Allocate device output memory
CUdeviceptr result = getCUdeviceptr((long)size());
arguments[arguments.length-1] = Pointer.to(result);
int blockSizeX = 256;
int gridSizeX = (int)Math.ceil((double)size() / blockSizeX);
callCudaFunction(function, arguments, gridSizeX, blockSizeX, 0);
return result;
}
private CUdeviceptr callCudaFunction(final CUfunction function, Pointer[] arguments, final int gridSizeX, final int blockSizeX, final int sharedMemorySize) {
// Allocate device output memory
CUdeviceptr result = getCUdeviceptr((long)size());
arguments[arguments.length-1] = Pointer.to(result);
// Set up the kernel parameters: A pointer to an array
// of pointers which point to the actual values.
Pointer kernelParameters = Pointer.to(arguments);
// Call the kernel function.
cuLaunchKernel(function,
gridSizeX, 1, 1, // Grid dimension
blockSizeX, 1, 1, // Block dimension
sharedMemorySize, null, // Shared memory size and stream
kernelParameters, null // Kernel- and extra parameters
);
cuCtxSynchronize();
return result;
}
/**
* The extension of the given file name is replaced with "ptx".
* If the file with the resulting name does not exist, it is
* compiled from the given file using NVCC. The name of the
* PTX file is returned.
*
* @param cuFileName The name of the .CU file
* @return The name of the PTX file
* @throws IOException If an I/O error occurs
*/
private static String preparePtxFile(String cuFileName) throws IOException
{
int endIndex = cuFileName.lastIndexOf('.');
if (endIndex == -1)
{
endIndex = cuFileName.length()-1;
}
String ptxFileName = cuFileName.substring(0, endIndex+1)+"ptx";
File ptxFile = new File(ptxFileName);
if (ptxFile.exists())
return ptxFileName;
File cuFile = new File(cuFileName);
if (!cuFile.exists())
throw new IOException("Input file not found: "+cuFileName);
String modelString = "-m"+System.getProperty("sun.arch.data.model");
String command =
"nvcc " + modelString + " -ptx "+
cuFile.getPath()+" -o "+ptxFileName;
System.out.println("Executing\n"+command);
Process process = Runtime.getRuntime().exec(command);
String errorMessage =
new String(toByteArray(process.getErrorStream()));
String outputMessage =
new String(toByteArray(process.getInputStream()));
int exitValue = 0;
try
{
exitValue = process.waitFor();
}
catch (InterruptedException e)
{
Thread.currentThread().interrupt();
throw new IOException(
"Interrupted while waiting for nvcc output", e);
}
if (exitValue != 0)
{
System.out.println("nvcc process exitValue "+exitValue);
System.out.println("errorMessage:\n"+errorMessage);
System.out.println("outputMessage:\n"+outputMessage);
throw new IOException(
"Could not create .ptx file: "+errorMessage);
}
System.out.println("Finished creating PTX file");
return ptxFileName;
}
/**
* Fully reads the given InputStream and returns it as a byte array
*
* @param inputStream The input stream to read
* @return The byte array containing the data from the input stream
* @throws IOException If an I/O error occurs
*/
private static byte[] toByteArray(InputStream inputStream)
throws IOException
{
ByteArrayOutputStream baos = new ByteArrayOutputStream();
byte buffer[] = new byte[8192];
while (true)
{
int read = inputStream.read(buffer);
if (read == -1)
{
break;
}
baos.write(buffer, 0, read);
}
return baos.toByteArray();
}
}
