The matrix (A) used for testing was a 3d Laplacian matrix obtained by using the 7 point stencil on a 20x20x20 grid => the matrix is of size 8000 x 8000. This is a banded matrix with 7 diagonals.
The RHS (b) was obtained by multiplying A by a vector of 1's. I wanted to solve Ax=b by GMRES
For both PETSC and CUSP I timed only the time taken for 200 iterations of the GMRES solver (ie time taken for reading in the matrix is not counted)
On Petsc the timings were done with PetscGetTime() and on Cusp with Cuda events.
Petsc (1 cpu) took 0.0558379 sec to do 200 iterations of GMRES.
Cusp took 0.1183 sec to do the same.
As you can see Petsc is 2.14 times faster than the Cusp library even on a single processor. The speed gap gets larger (obviously) on using more than 1 processor.
This experiment was done on a GTX 570 card on an Ubuntu 10.10 running CUDA 4.0 having cusp v 0.3.1 and thrust 1.6.0
I have read this paper by one of the authors of CUSP. However, they have not compared the performance of CUSP with Petsc in it.
The paper also says that the data-structure used for storing the sparse matrix matters. Accordingly they have provided several formats like DIA, ELL, HYB, COO etc. but even after I tried them all, the Cusp GMRES performance does not change and still takes 0.1183 seconds for the 200 iterations.
Here is the (quite-short) GPU code on pastebin I used while using CUSP. I am not sure if I am using the Cusp library in an optimal manner.
Although it is possible PETSc is still genuinely better than CUSP I want to know if significant speedup is possible with CUSP if used properly.
If more information is needed to answer this question please let me know. Thank you.