I didn’t have that problem when running the test on a system with Tesla V100-32GB and 128GB of system memory:

```
$ /usr/local/cuda/samples/7_CUDALibraries/cuSolverSp_LinearSolver/cuSolverSp_LinearSolver --file=ML_Laplace.mtx
GPU Device 0: "Tesla V100-PCIE-32GB" with compute capability 7.0
Using input file [ML_Laplace.mtx]
step 1: read matrix market format
sparse matrix A is 377002 x 377002 with 27689972 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
WARNING: the matrix is singular at row 1 under tol (1.000000E-12)
step 6: evaluate residual r = b - A*x (result on CPU)
(CPU) |b - A*x| = NAN
(CPU) |A| = 5.149075E+07
(CPU) |x| = NAN
(CPU) |b| = 1.999997E+00
(CPU) |b - A*x|/(|A|*|x| + |b|) = NAN
step 7: solve A*x = b on GPU
WARNING: the matrix is singular at row 2 under tol (1.000000E-12)
step 8: evaluate residual r = b - A*x (result on GPU)
(GPU) |b - A*x| = NAN
(GPU) |A| = 5.149075E+07
(GPU) |x| = NAN
(GPU) |b| = 1.999997E+00
(GPU) |b - A*x|/(|A|*|x| + |b|) = NAN
timing chol: CPU = 1777.050997 sec , GPU = 82.648804 sec
show last 10 elements of solution vector (GPU)
consistent result for different reordering and solver
x[376992] = -NAN
x[376993] = -NAN
x[376994] = -NAN
x[376995] = -NAN
x[376996] = -NAN
x[376997] = -NAN
x[376998] = -NAN
x[376999] = -NAN
x[377000] = -NAN
x[377001] = -NAN
$
```

I assume the NAN values may be due to the singularity warnings, but haven’t investigated it.

I’m not sure where the breaking points would be in terms of memory size, and I don’t have any suggestions for predicting memory size from matrix size, except that larger matrices will require more memory. The error is a good indication that you don’t have enough memory.