Satish,

I have reconfigured the PETSC with –download-mpich=1 and –with-device=ch3:sock. The results show that the speed up can now remain increasing when computing cores increase from 1 to 16. However, the maximum speed up is still only around 6.0 with 16 cores. The new log files can be found in the attachment.

(1)

I checked the configuration of the first server again. This server is a shared-memory computer, with

Processors: 4 CPUS * 4Cores/CPU, with each core 2500MHz

Memories: 16 *2 GB DDR2 333 MHz, dual channel, data width 64 bit, so the memory Bandwidth for 2 memories is 64/8*166*2*2=5.4GB/s.

It seems that each core can get 2.7GB/s memory bandwidth which can fulfill the basic requirement for sparse iterative solvers.

Is this correct? Does the shared-memory type of computer have no benefit for PETSC when the memory bandwidth is limited?

(2)

Beside, we would like to continue our work by employing a matrix partitioning / reordering algorithm, such as Metis or ParMetis, to improve the speed up performance of the program. (The current program works without any matrix decomposition.)

Matt, as you said in http://lists.mcs.anl.gov/pipermail/petsc-users/2007-January/001017.html ,“Reordering a matrix can result in fewer iterations for an iterative solver“.

Do you think the matrix partitioning/reordering will work for this program? Or any further suggestions?

Any comments are very welcome! Thank you!