def monte_carlo_pricer(paths, dt, interest, volatility): n = paths.shape[0] mm = MM(shape=n, dtype=np.double, prealloc=5) blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK gridsz = int(math.ceil(float(n) / blksz)) stream = cuda.stream() prng = PRNG(PRNG.MRG32K3A, stream=stream) # Allocate device side array d_normdist = cuda.device_array(n, dtype=np.double, stream=stream) c0 = interest - 0.5 * volatility ** 2 c1 = volatility * math.sqrt(dt) d_last = cuda.to_device(paths[:, 0], to=mm.get()) for j in range(1, paths.shape[1]): prng.normal(d_normdist, mean=0, sigma=1) d_paths = cuda.to_device(paths[:, j], stream=stream, to=mm.get()) step(d_last, dt, c0, c1, d_normdist, out=d_paths, stream=stream) d_paths.copy_to_host(paths[:, j], stream=stream) mm.free(d_last) d_last = d_paths stream.synchronize()
def monte_carlo_pricer(paths, dt, interest, volatility): n = paths.shape[0] mm = MM(shape=n, dtype=np.double, prealloc=5) blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK gridsz = int(math.ceil(float(n) / blksz)) stream = cuda.stream() prng = PRNG(PRNG.MRG32K3A, stream=stream) # Allocate device side array d_normdist = cuda.device_array(n, dtype=np.double, stream=stream) c0 = interest - 0.5 * volatility**2 c1 = volatility * math.sqrt(dt) d_last = cuda.to_device(paths[:, 0], to=mm.get()) for j in range(1, paths.shape[1]): prng.normal(d_normdist, mean=0, sigma=1) d_paths = cuda.to_device(paths[:, j], stream=stream, to=mm.get()) step(d_last, dt, c0, c1, d_normdist, out=d_paths, stream=stream) d_paths.copy_to_host(paths[:, j], stream=stream) mm.free(d_last) d_last = d_paths stream.synchronize()
def getPseudoRandomNumbers_Standard_cuda(shape=tuple): # type: (object) -> object """ generates a an array of psuedo random numbers from standard normal distribution using CUDA :rtype: ndarray :param length: :return: """ prng = PRNG(rndtype=PRNG.XORWOW) rand = empty(shape) prng.normal(rand, 0, 1) return rand
def monte_carlo_pricer(paths, dt, interest, volatility): n = paths.shape[0] blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK gridsz = int(math.ceil(float(n) / blksz)) # Instantiate cuRAND PRNG prng = PRNG(PRNG.MRG32K3A) # Allocate device side array d_normdist = cuda.device_array(n, dtype=np.double) c0 = interest - 0.5 * volatility ** 2 c1 = volatility * math.sqrt(dt) # Simulation loop d_last = cuda.to_device(paths[:, 0]) for j in range(1, paths.shape[1]): prng.normal(d_normdist, mean=0, sigma=1) d_paths = cuda.to_device(paths[:, j]) step(d_last, dt, c0, c1, d_normdist, out=d_paths) d_paths.copy_to_host(paths[:, j]) d_last = d_paths
def monte_carlo_pricer(paths, dt, interest, volatility): n = paths.shape[0] blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK gridsz = int(math.ceil(float(n) / blksz)) # Instantiate cuRAND PRNG prng = PRNG(PRNG.MRG32K3A) # Allocate device side array d_normdist = cuda.device_array(n, dtype=np.double) c0 = interest - 0.5 * volatility**2 c1 = volatility * math.sqrt(dt) # Simulation loop d_last = cuda.to_device(paths[:, 0]) for j in range(1, paths.shape[1]): prng.normal(d_normdist, mean=0, sigma=1) d_paths = cuda.to_device(paths[:, j]) step(d_last, dt, c0, c1, d_normdist, out=d_paths) d_paths.copy_to_host(paths[:, j]) d_last = d_paths