GPU Device 0: "Hopper" with compute capability 9.0

Using default input file [../../../../Samples/4_CUDA_Libraries/cuSolverSp_LinearSolver/lap2D_5pt_n100.mtx]
step 1: read matrix market format
sparse matrix A is 10000 x 10000 with 49600 nonzeros, base=1
step 2: reorder the matrix A to minimize zero fill-in
        if the user choose a reordering by -P=symrcm, -P=symamd or -P=metis
step 2.1: no reordering is chosen, Q = 0:n-1 
step 2.2: B = A(Q,Q) 
step 3: b(j) = 1 + j/n 
step 4: prepare data on device
step 5: solve A*x = b on CPU 
step 6: evaluate residual r = b - A*x (result on CPU)
(CPU) |b - A*x| = 5.393685E-12 
(CPU) |A| = 8.000000E+00 
(CPU) |x| = 1.136492E+03 
(CPU) |b| = 1.999900E+00 
(CPU) |b - A*x|/(|A|*|x| + |b|) = 5.931079E-16 
step 7: solve A*x = b on GPU
step 8: evaluate residual r = b - A*x (result on GPU)
(GPU) |b - A*x| = 1.970424E-12 
(GPU) |A| = 8.000000E+00 
(GPU) |x| = 1.136492E+03 
(GPU) |b| = 1.999900E+00 
(GPU) |b - A*x|/(|A|*|x| + |b|) = 2.166745E-16 
timing chol: CPU =   0.097956 sec , GPU =   0.103812 sec
show last 10 elements of solution vector (GPU) 
consistent result for different reordering and solver 
x[9990] = 3.000016E+01
x[9991] = 2.807343E+01
x[9992] = 2.601354E+01
x[9993] = 2.380285E+01
x[9994] = 2.141866E+01
x[9995] = 1.883070E+01
x[9996] = 1.599668E+01
x[9997] = 1.285365E+01
x[9998] = 9.299423E+00
x[9999] = 5.147265E+00