def main():
loops = int(sys.argv[1])
n = np.random.randint(0, 1234)
num_samps = 2**16
cpu_sig, gpu_sig = rand_data_gen_gpu(num_samps)
# Run baseline with scipy.signal.gauss_spline
with prof.time_range("scipy_gauss_spline", 0):
cpu_gauss_spline = signal.gauss_spline(cpu_sig, n)
# Run CuPy version
with prof.time_range("cupy_gauss_spline", 1):
gpu_gauss_spline = gauss_spline(gpu_sig, n)
# Compare results
np.testing.assert_allclose(cpu_gauss_spline, cp.asnumpy(gpu_gauss_spline),
1e-3)
# Run multiple passes to get average
for _ in range(loops):
with prof.time_range("cupy_gauss_spline_loop", 2):
gpu_gauss_spline = gauss_spline(gpu_sig, n)
cp.cuda.runtime.deviceSynchronize()
def main():
loops = int(sys.argv[1])
n = np.random.randint(0, 1234)
num_samps = 2**16
cpu_sig = rand_data_gen(num_samps)
# Run baseline with scipy.signal.gauss_spline
with prof.time_range("scipy_gauss_spline", 0):
cpu_gauss_spline = signal.gauss_spline(cpu_sig, n)
# Run multiple passes to get average
for _ in range(loops):
with prof.time_range("scipy_gauss_spline_loop", 0):
cpu_gauss_spline = signal.gauss_spline(cpu_sig, n)
# trying out the polynomials in scipy # fit a polynomial of specified order in least-squares sense # this works poorly import numpy as np import matplotlib.pyplot as plt from scipy import interpolate from scipy import signal import sys y = [] for line in sys.stdin: y.append(float(line)) x = np.arange(0,len(y)) poly = signal.gauss_spline(y,int(sys.argv[1])*2+1) print poly exit() ypoly = np.polyval(poly,x) plt.figure() plt.plot(x,y,'x') plt.plot(x,ypoly,'-g') plt.show()
def cpu_version(self, x, n):
return signal.gauss_spline(x, n)