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- //---------------------------------------------------------------------------//
- // Copyright (c) 2013-2014 Kyle Lutz <kyle.r.lutz@gmail.com>
- //
- // Distributed under the Boost Software License, Version 1.0
- // See accompanying file LICENSE_1_0.txt or copy at
- // http://www.boost.org/LICENSE_1_0.txt
- //
- // See http://boostorg.github.com/compute for more information.
- //---------------------------------------------------------------------------//
- #include <cstdlib>
- #include <iostream>
- #include <boost/compute/command_queue.hpp>
- #include <boost/compute/system.hpp>
- #include <boost/compute/algorithm/copy_n.hpp>
- #include <boost/compute/container/vector.hpp>
- #include <boost/compute/utility/source.hpp>
- namespace compute = boost::compute;
- // return a random float between lo and hi
- float rand_float(float lo, float hi)
- {
- float x = (float) std::rand() / (float) RAND_MAX;
- return (1.0f - x) * lo + x * hi;
- }
- // this example demostrates a black-scholes option pricing kernel.
- int main()
- {
- // number of options
- const int N = 4000000;
- // black-scholes parameters
- const float risk_free_rate = 0.02f;
- const float volatility = 0.30f;
- // get default device and setup context
- compute::device gpu = compute::system::default_device();
- compute::context context(gpu);
- compute::command_queue queue(context, gpu);
- std::cout << "device: " << gpu.name() << std::endl;
- // initialize option data on host
- std::vector<float> stock_price_data(N);
- std::vector<float> option_strike_data(N);
- std::vector<float> option_years_data(N);
- std::srand(5347);
- for(int i = 0; i < N; i++){
- stock_price_data[i] = rand_float(5.0f, 30.0f);
- option_strike_data[i] = rand_float(1.0f, 100.0f);
- option_years_data[i] = rand_float(0.25f, 10.0f);
- }
- // create memory buffers on the device
- compute::vector<float> call_result(N, context);
- compute::vector<float> put_result(N, context);
- compute::vector<float> stock_price(N, context);
- compute::vector<float> option_strike(N, context);
- compute::vector<float> option_years(N, context);
- // copy initial values to the device
- compute::copy_n(stock_price_data.begin(), N, stock_price.begin(), queue);
- compute::copy_n(option_strike_data.begin(), N, option_strike.begin(), queue);
- compute::copy_n(option_years_data.begin(), N, option_years.begin(), queue);
- // source code for black-scholes program
- const char source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
- // approximation of the cumulative normal distribution function
- static float cnd(float d)
- {
- const float A1 = 0.319381530f;
- const float A2 = -0.356563782f;
- const float A3 = 1.781477937f;
- const float A4 = -1.821255978f;
- const float A5 = 1.330274429f;
- const float RSQRT2PI = 0.39894228040143267793994605993438f;
- float K = 1.0f / (1.0f + 0.2316419f * fabs(d));
- float cnd =
- RSQRT2PI * exp(-0.5f * d * d) *
- (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5)))));
- if(d > 0){
- cnd = 1.0f - cnd;
- }
- return cnd;
- }
- // black-scholes option pricing kernel
- __kernel void black_scholes(__global float *call_result,
- __global float *put_result,
- __global const float *stock_price,
- __global const float *option_strike,
- __global const float *option_years,
- float risk_free_rate,
- float volatility)
- {
- const uint opt = get_global_id(0);
- float S = stock_price[opt];
- float X = option_strike[opt];
- float T = option_years[opt];
- float R = risk_free_rate;
- float V = volatility;
- float sqrtT = sqrt(T);
- float d1 = (log(S / X) + (R + 0.5f * V * V) * T) / (V * sqrtT);
- float d2 = d1 - V * sqrtT;
- float CNDD1 = cnd(d1);
- float CNDD2 = cnd(d2);
- float expRT = exp(-R * T);
- call_result[opt] = S * CNDD1 - X * expRT * CNDD2;
- put_result[opt] = X * expRT * (1.0f - CNDD2) - S * (1.0f - CNDD1);
- }
- );
- // build black-scholes program
- compute::program program = compute::program::create_with_source(source, context);
- program.build();
- // setup black-scholes kernel
- compute::kernel kernel(program, "black_scholes");
- kernel.set_arg(0, call_result);
- kernel.set_arg(1, put_result);
- kernel.set_arg(2, stock_price);
- kernel.set_arg(3, option_strike);
- kernel.set_arg(4, option_years);
- kernel.set_arg(5, risk_free_rate);
- kernel.set_arg(6, volatility);
- // execute black-scholes kernel
- queue.enqueue_1d_range_kernel(kernel, 0, N, 0);
- // print out the first option's put and call prices
- float call0, put0;
- compute::copy_n(put_result.begin(), 1, &put0, queue);
- compute::copy_n(call_result.begin(), 1, &call0, queue);
- std::cout << "option 0 call price: " << call0 << std::endl;
- std::cout << "option 0 put price: " << put0 << std::endl;
- // due to the differences in the random-number generators between Operating Systems
- // and/or compilers, we will get different "expected" results for this example
- #ifdef __APPLE__
- double expected_call0 = 0.000249461;
- double expected_put0 = 26.2798;
- #elif _MSC_VER
- double expected_call0 = 8.21412;
- double expected_put0 = 2.25904;
- #else
- double expected_call0 = 0.0999f;
- double expected_put0 = 43.0524f;
- #endif
- // check option prices
- if(std::abs(call0 - expected_call0) > 1e-4 || std::abs(put0 - expected_put0) > 1e-4){
- std::cerr << "error: option prices are wrong" << std::endl;
- return -1;
- }
- return 0;
- }
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