Hi. I ran across the same issue before, but after some tests, I still can't beat the speed of the built in K=Sparse(I,J,V). Actually, if the local element matrices are expensive, one can make the assembly of I, J and V in parallel, but the overall speed will depend on the memory copy.
On Wednesday, October 21, 2015 at 5:11:35 AM UTC-2, mac wrote: Hi, > > I am trying to speedup my Julia finite element code. Right now I use the > built in sparse solver to solve the linear system in parallel and the > solving step is very fast. But my system matrix assembly is done serially > using single process and its slow. I would like to speed up by assembling > the system matrix and vector in parallel. I am executing the code using a > shared memory machine (12 core workstation). Can someone give me a very > simple example to do the following to help me get started: > > Lets say, we have three matrices: A (dense) and B (dense) and C (sparse). > All 3 can be shared arrays. I would like to have several processes running > in parallel to fetch a set of elements from A and B, do some simple > arithmetic and store the results into the sparse matrix C. > > I am treating 'A' as a matrix containing nodal co-ordinates and B > containing the element info. Using the example, I would eventually convert > my code such that each process computes an element matrix and assemble into > the big sparse system matrix in parallel. Is this approach efficient ? > > Thank you. >
