def test_cublasCsscal(self):
x = (np.random.rand(5)+1j*np.random.rand(5)).astype(np.complex64)
x_gpu = gpuarray.to_gpu(x)
alpha = np.float32(np.random.rand())
cublas.cublasCscal(self.cublas_handle, x_gpu.size, alpha,
x_gpu.gpudata, 1)
assert np.allclose(x_gpu.get(), alpha*x)
def bicgstabMemory(cublasHandle, x_gpu, b_gpu, Xprime_gpu, X_gpu, XX_gpu,
Yprime_gpu, Y_gpu, YY_gpu, Zprime_gpu, zzero, freq,
FREQ_gpu, c, Deltaxprime, Deltayprime, Deltazprime,
sizePartitionr, sizePartitionc, M, max_it, tol):
# --- flag: 0 = solution found to tolerance
# 1 = no convergence given max_it
# -1 = breakdown: rho = 0
# -2 = breakdown: omega = 0
N = xcg_gpu.size
# --- Initializations
iter = np.float32(0)
flag = np.float32(0)
alpha = np.float32(0)
rho_1 = np.float32(0)
v_gpu = gpuarray.zeros(N, dtype=np.float32)
p_gpu = gpuarray.zeros(N, dtype=np.float32)
# d_p_hat = gpuarray.zeros(N, dtype = np.float32)
# d_s_hat = gpuarray.zeros(N, dtype = np.float32)
# d_t = gpuarray.zeros(N, dtype = np.float32)
#bnrm2 = np.sqrt((culinalg.dot(b_gpu, b_gpu.conj(), 'T', 'N').real).get())
bnrm2 = cublas.cublasScnrm2(cublasHandle, N, b_gpu.gpudata, 1)
if bnrm2 == np.float32(0.0):
bnrm2 = np.float32(1.0)
yprime_gpu = computeAx(x_gpu, Xprime_gpu, X_gpu, XX_gpu, Yprime_gpu, Y_gpu,
YY_gpu, Zprime_gpu, zzero, freq, FREQ_gpu, c,
Deltaxprime, Deltayprime, Deltazprime,
sizePartitionc, XX_gpu.size)
xprime_gpu = computeAdy(cublasHandle, yprime_gpu, Xprime_gpu, XX_gpu,
Yprime_gpu, YY_gpu, Zprime_gpu, zzero, FREQ_gpu, c,
Deltaxprime * Deltayprime * Deltazprime,
sizePartitionr, b_gpu.size)
r_gpu = b_gpu - xprime_gpu
error = cublas.cublasScnrm2(cublasHandle, N, r_gpu.gpudata, 1) / bnrm2
if (error < tol):
return x_gpu, error, iter, flag
omega = np.float32(1.0)
r_tld_gpu = r_gpu.copy()
for iter in range(max_it):
rho = cublas.cublasCdotc(cublasHandle, N, r_tld_gpu.gpudata, 1,
r_gpu.gpudata, 1) # direction vector
if (rho == np.float32(0.0)):
break
if (iter > 0):
beta = (rho / rho_1) * (alpha / omega)
cublas.cublasCaxpy(cublasHandle, N, -omega, v_gpu.gpudata, 1,
p_gpu.gpudata, 1)
cublas.cublasCscal(cublasHandle, N, beta, p_gpu.gpudata, 1)
cublas.cublasCaxpy(cublasHandle, N, np.float32(1.0), r_gpu.gpudata,
1, p_gpu.gpudata, 1)
else:
p_gpu = r_gpu.copy()
p_hat_gpu = p_gpu.copy()
yprime_gpu = computeAx(p_hat_gpu, Xprime_gpu, X_gpu, XX_gpu,
Yprime_gpu, Y_gpu, YY_gpu, Zprime_gpu, zzero,
freq, FREQ_gpu, c, Deltaxprime, Deltayprime,
Deltazprime, sizePartitionc, XX_gpu.size)
v_gpu = computeAdy(cublasHandle, yprime_gpu, Xprime_gpu, XX_gpu,
Yprime_gpu, YY_gpu, Zprime_gpu, zzero, FREQ_gpu, c,
Deltaxprime * Deltayprime * Deltazprime,
sizePartitionr, b_gpu.size)
alpha = rho / cublas.cublasCdotc(cublasHandle, N, r_tld_gpu.gpudata, 1,
v_gpu.gpudata, 1)
s_gpu = r_gpu.copy()
cublas.cublasCaxpy(cublasHandle, N, -alpha, v_gpu.gpudata, 1,
s_gpu.gpudata, 1)
norms = cublas.cublasScnrm2(cublasHandle, N, s_gpu.gpudata, 1)
if (norms < tol): # --- early convergence check
cublas.cublasCaxpy(cublasHandle, N, np.float32(alpha),
p_hat_gpu.gpudata, 1, x_gpu.gpudata, 1)
break
# --- stabilizer
s_hat_gpu = s_gpu.copy()
yprime_gpu = computeAx(s_hat_gpu, Xprime_gpu, X_gpu, XX_gpu,
Yprime_gpu, Y_gpu, YY_gpu, Zprime_gpu, zzero,
freq, FREQ_gpu, c, Deltaxprime, Deltayprime,
Deltazprime, sizePartitionc, XX_gpu.size)
t_gpu = computeAdy(cublasHandle, yprime_gpu, Xprime_gpu, XX_gpu,
Yprime_gpu, YY_gpu, Zprime_gpu, zzero, FREQ_gpu, c,
Deltaxprime * Deltayprime * Deltazprime,
sizePartitionr, b_gpu.size)
omega = cublas.cublasCdotc(cublasHandle, N, t_gpu.gpudata, 1,
s_gpu.gpudata, 1) / cublas.cublasCdotc(
cublasHandle, N, t_gpu.gpudata, 1,
t_gpu.gpudata, 1)
# --- update approximation
cublas.cublasCaxpy(cublasHandle, N, alpha, p_hat_gpu.gpudata, 1,
x_gpu.gpudata, 1)
cublas.cublasCaxpy(cublasHandle, N, omega, s_hat_gpu.gpudata, 1,
x_gpu.gpudata, 1)
r_gpu = s_gpu.copy()
cublas.cublasCaxpy(cublasHandle, N, -omega, t_gpu.gpudata, 1,
r_gpu.gpudata, 1)
error = cublas.cublasScnrm2(cublasHandle, N, r_gpu.gpudata, 1) / bnrm2
# --- check convergence
if (error <= tol):
break
if (omega == np.float32(0.0)):
break
rho_1 = rho
print("iteration")
temp = np.sqrt(
gpuarray.max(s_gpu.real * s_gpu.real + s_gpu.imag * s_gpu.imag).get())
if ((error <= np.float32(tol)) or temp <= tol): # --- converged
if (temp <= tol):
error = cublas.cublasScnrm2(cublasHandle, N, s_gpu.gpudata,
1) / bnrm2
flag = 0
elif (omega == np.float32(0.0)): # --- breakdown
flag = -2
elif (rho == np.float32(0.0)):
flag = -1
else: # --- no convergence
flag = 1
p_hat_gpu.gpudata.free()
s_hat_gpu.gpudata.free()
v_gpu.gpudata.free()
t_gpu.gpudata.free()
return xcg_gpu, 0, 0, 0
