int read_tid() {
// CHECK: call i32 @llvm.ptx.read.tid.x()
// CHECK: call i32 @llvm.ptx.read.tid.y()
// CHECK: call i32 @llvm.ptx.read.tid.z()
// CHECK: call i32 @llvm.ptx.read.tid.w()
int x = __builtin_ptx_read_tid_x();
int y = __builtin_ptx_read_tid_y();
int z = __builtin_ptx_read_tid_z();
int w = __builtin_ptx_read_tid_w();
return x + y + z + w;
}
void BlackScholesGPU(
float *d_CallResult,
float *d_PutResult,
float *d_StockPrice,
float *d_OptionStrike,
float *d_OptionYears,
float Riskfree,
float Volatility,
int optN
)
{
//Thread index
const int tid = (__builtin_ptx_read_ctaid_x() * __builtin_ptx_read_ntid_x()) + __builtin_ptx_read_tid_x();
//Total number of threads in execution grid
const int THREAD_N = __builtin_ptx_read_nctaid_x() * __builtin_ptx_read_ntid_x();
//No matter how small is execution grid or how large OptN is,
//exactly OptN indices will be processed with perfect memory coalescing
for (int opt = tid; opt < optN; opt += THREAD_N) {
float sqrtT, expRT;
float d1, d2, CNDD1, CNDD2;
sqrtT = sqrtf(T);
d1 = (__logf(S / X) + (R + 0.5f * V * V) * T) / (V * sqrtT);
d2 = d1 - V * sqrtT;
//CNDD1 = cndGPU(d1);
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 = 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)
CNDD1 = 1.0f - cnd;
//CNDD2 = cndGPU(d2);
K = 1.0f / (1.0f + 0.2316419f * fabsf(d));
cnd = RSQRT2PI * __expf(- 0.5f * d * d) *
(K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5)))));
if (d > 0)
CNDD2 = 1.0f - cnd;
//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);
}
}
