cuda-samples/Samples/BlackScholes_nvrtc/BlackScholes_kernel.cuh

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2021-10-21 19:04:49 +08:00
/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
///////////////////////////////////////////////////////////////////////////////
// Polynomial approximation of cumulative normal distribution function
///////////////////////////////////////////////////////////////////////////////
__device__ inline float cndGPU(float d) {
const float A1 = 0.31938153f;
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 = __fdividef(1.0f, (1.0f + 0.2316419f * fabsf(d)));
float cnd = RSQRT2PI * __expf(-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 formula for both call and put
///////////////////////////////////////////////////////////////////////////////
__device__ inline void BlackScholesBodyGPU(float &CallResult, float &PutResult,
float S, // Stock price
float X, // Option strike
float T, // Option years
float R, // Riskless rate
float V // Volatility rate
) {
float sqrtT, expRT;
float d1, d2, CNDD1, CNDD2;
sqrtT = __fdividef(1.0F, rsqrtf(T));
d1 = __fdividef(__logf(S / X) + (R + 0.5f * V * V) * T, V * sqrtT);
d2 = d1 - V * sqrtT;
CNDD1 = cndGPU(d1);
CNDD2 = cndGPU(d2);
// Calculate Call and Put simultaneously
expRT = __expf(-R * T);
CallResult = S * CNDD1 - X * expRT * CNDD2;
PutResult = X * expRT * (1.0f - CNDD2) - S * (1.0f - CNDD1);
}
////////////////////////////////////////////////////////////////////////////////
// Process an array of optN options on GPU
////////////////////////////////////////////////////////////////////////////////
extern "C" __launch_bounds__(128) __global__
void BlackScholesGPU(float2 *__restrict d_CallResult,
float2 *__restrict d_PutResult,
float2 *__restrict d_StockPrice,
float2 *__restrict d_OptionStrike,
float2 *__restrict d_OptionYears, float Riskfree,
float Volatility, int optN) {
////Thread index
const int opt = blockDim.x * blockIdx.x + threadIdx.x;
// Calculating 2 options per thread to increase ILP (instruction level
// parallelism)
if (opt < (optN / 2)) {
float callResult1, callResult2;
float putResult1, putResult2;
BlackScholesBodyGPU(callResult1, putResult1, d_StockPrice[opt].x,
d_OptionStrike[opt].x, d_OptionYears[opt].x, Riskfree,
Volatility);
BlackScholesBodyGPU(callResult2, putResult2, d_StockPrice[opt].y,
d_OptionStrike[opt].y, d_OptionYears[opt].y, Riskfree,
Volatility);
d_CallResult[opt] = make_float2(callResult1, callResult2);
d_PutResult[opt] = make_float2(putResult1, putResult2);
}
}