barbagroup / fmm-bem-relaxed Goto Github PK

According to the relaxation strategy that $p \sim \log_2 (\epsilon)$, we should use std::log2 instead of std::log in predict_p function.

I am not sure if this repository / the research work is actively maintained. I am following your paper and this code for my research work. But i have trouble in making it work for mixed and robin boundary conditions since you only have examples that deal with one specific type of boundary condition in a run.

If there is any example (even if its very elementary) would be really useful.

Thanks in advance.

I am trying to use the code to reproduce the speedup for Laplace 1st-kind problem. So I ran the first non-relaxed case (corresponding to the non-relaxed case on the first row of the table below) using:
theta = 0.5, N = 2048 (recursions = 5) , ncrit = 400

In the terminal, I first ran (without preconditioning)
./LaplaceBEM -theta 0.5 -ncrit 400 -p 8 -recursions 5 -fixed_p -gmres -solver_tol 1e-5
and I got the result like this:

initialised 2048 triangles
...
Solver: GMRES
Preconditioner: Identity
it: 001, res: 2.311e-04, fmm_req_p: 8
it: 002, res: 9.630e-05, fmm_req_p: 8
it: 003, res: 4.506e-05, fmm_req_p: 8
it: 004, res: 1.935e-05, fmm_req_p: 8
Final residual: 9.2838e-06, after 5 iterations

TIMING:
    setup : 3.6045e+00s
    solve : 1.5399e+01s
external phi: 0.192, exact: 0.19245, error: 2.3505e-03
error: 5.344e-03

The answer seemed to be correct, so then I tried to used the diagonal matrix as the preconditioner (try to match the "t_solve = 0.27" in the table):
./LaplaceBEM -theta 0.5 -ncrit 400 -p 8 -recursions 5 -fixed_p -gmres -diagonal -solver_tol 1e-5
I got this:

initialised 2048 triangles
......
Creating plan: 0.00376582
Executing plan: 3.58662
1st-kind equation being solved

TIMING:
    setup : 3.5906e+00s
    solve : 4.9171e-03s
external phi: 2.1197e-10, exact: 0.19245, error: 1.0000e+00
error: 1.000e+00

it seems that the preconditioner is not working this time. I tried the "local" preconditioned with "fgmres" solver, and got the same error. I compiled the code using the Makefile in the "examples" folder, and gave me no error on LaplaceBEM executable. And the gcc version on phantom is 4.6.
Would you @slayton58 help me out on how to use the diagonal preconditioner correctly to get a similar performance of t_solve = 0.27 in this case?
Thank you.

Tingyu

I used massif tool in valgrind to monitor the heap usage of StokesBEM running on phantom (for 4 threads). I didn't recorded the stack usage for StokesBEM since it would slow down valgrind a lot for a large problem size (recursions=6).

In the figures below, x-axis shows the number of instructions executed, y-axis gives how much heap memory is used.

The heap usage peaks when the code is generating RHS. There is an almost doubled memory usage at that point. When I enable lazy evaluation, I think it is creating the RHS plan that doubles the memory usage. (line 198 in LaplaceBEM.cpp)

FMM_plan<kernel_type> rhs_plan = FMM_plan<kernel_type>(K,panels,opts);

I read the class definition of FMM_plan but could not find where the code needs to allocate extra heap. Would you recall and help me on that? @slayton58 Thanks.

Here is the stack+heap usage on a smaller case (recursions=4). I found a similar peak.