On Tue, Apr 20, 2021 at 10:41 AM Francesco Brarda <[email protected]> wrote:
> I was trying to follow Barry's advice some time ago, but I guess that's > not the way he meant it. How should I refer to the values contained in x? > With Distributed Arrays? > That is how you get values from x. However, I cannot understand at all what you are doing with "mybase". Matt > Thanks > Francesco > > Even though it will not scale and will deliver slower performance it is >> completely possible for you to solve the 3 variable problem using 3 MPI >> ranks. Or 10 mpi ranks. You would just create vectors/matrices with 1 >> degree of freedom for the first three ranks and no degrees of freedom for >> the later ranks. During your function evaluation (and Jacobian evaluation) >> for TS you will need to set up the appropriate communication to get the >> values you need on each rank to evaluate the parts of the function >> evaluation needed by that rank. This is true for parallelizing any >> computation. >> >> Barry >> >> > > > Il giorno 20 apr 2021, alle ore 15:40, Matthew Knepley <[email protected]> > ha scritto: > > On Tue, Apr 20, 2021 at 9:36 AM Francesco Brarda < > [email protected]> wrote: > >> Hi! >> I tried to implement the SIR model taking into account the fact that I >> will only use 3 MPI ranks at this moment. >> I built vectors and matrices following the examples already available. In >> particular, I defined the functions required similarly (RHSFunction, >> IFunction, IJacobian), as follows: >> > > I don't think this makes sense. You use "mybase" to distinguish between 3 > procs, which would indicate that each procs has only > 1 degree of freedom. However, you use x[1] on each proc, indicating it has > at least 2 dofs. > > Thanks, > > Matt > > >> static PetscErrorCode RHSFunction(TS ts,PetscReal t,Vec X,Vec F,void *ctx) >> { >> PetscErrorCode ierr; >> AppCtx *appctx = (AppCtx*) ctx; >> PetscScalar f;//, *x_localptr; >> const PetscScalar *x; >> PetscInt mybase; >> >> PetscFunctionBeginUser; >> ierr = VecGetOwnershipRange(X,&mybase,NULL);CHKERRQ(ierr); >> ierr = VecGetArrayRead(X,&x);CHKERRQ(ierr); >> if (mybase == 0) { >> f = (PetscScalar) (-appctx->p1*x[0]*x[1]/appctx->N); >> ierr = VecSetValues(F,1,&mybase,&f,INSERT_VALUES); >> } >> if (mybase == 1) { >> f = (PetscScalar) (appctx->p1*x[0]*x[1]/appctx->N-appctx->p2*x[1]); >> ierr = VecSetValues(F,1,&mybase,&f,INSERT_VALUES); >> } >> if (mybase == 2) { >> f = (PetscScalar) (appctx->p2*x[1]); >> ierr = VecSetValues(F,1,&mybase,&f,INSERT_VALUES); >> } >> ierr = VecRestoreArrayRead(X,&x);CHKERRQ(ierr); >> ierr = VecAssemblyBegin(F);CHKERRQ(ierr); >> ierr = VecAssemblyEnd(F);CHKERRQ(ierr); >> PetscFunctionReturn(0); >> } >> >> >> Whilst for the Jacobian I did: >> >> >> static PetscErrorCode IJacobian(TS ts,PetscReal t,Vec X,Vec >> Xdot,PetscReal a,Mat A,Mat B,void *ctx) >> { >> PetscErrorCode ierr; >> AppCtx *appctx = (AppCtx*) ctx; >> PetscInt mybase, rowcol[] = {0,1,2}; >> const PetscScalar *x; >> >> PetscFunctionBeginUser; >> ierr = MatGetOwnershipRange(B,&mybase,NULL);CHKERRQ(ierr); >> ierr = VecGetArrayRead(X,&x);CHKERRQ(ierr); >> if (mybase == 0) { >> const PetscScalar J[] = {a + appctx->p1*x[1]/appctx->N, >> appctx->p1*x[0]/appctx->N, 0}; >> ierr = >> MatSetValues(B,1,&mybase,3,rowcol,J,INSERT_VALUES);CHKERRQ(ierr); >> } >> if (mybase == 1) { >> const PetscScalar J[] = {- appctx->p1*x[1]/appctx->N, a - >> appctx->p1*x[0]/appctx->N + appctx->p2, 0}; >> ierr = >> MatSetValues(B,1,&mybase,3,rowcol,J,INSERT_VALUES);CHKERRQ(ierr); >> } >> if (mybase == 2) { >> const PetscScalar J[] = {0, - appctx->p2, a}; >> ierr = >> MatSetValues(B,1,&mybase,3,rowcol,J,INSERT_VALUES);CHKERRQ(ierr); >> } >> ierr = VecRestoreArrayRead(X,&x);CHKERRQ(ierr); >> >> ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> if (A != B) { >> ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> } >> PetscFunctionReturn(0); >> } >> >> This code does not provide the correct result, that is, the solution is >> the initial condition, either using implicit or explicit methods. Is the >> way I defined these objects wrong? How can I fix it? >> I also tried to print the Jacobian with the following commands but it >> does not work (blank rows and error message). How should I print the >> Jacobian? >> >> ierr = TSGetIJacobian(ts,NULL,&K, NULL, NULL); CHKERRQ(ierr); >> ierr = MatAssemblyBegin(K,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> ierr = MatAssemblyEnd(K,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); >> ierr = MatView(K,PETSC_VIEWER_STDOUT_WORLD);CHKERRQ(ierr); >> >> >> I would very much appreciate any kind of help or advice. >> Best, >> Francesco >> >> Il giorno 2 apr 2021, alle ore 04:45, Barry Smith <[email protected]> ha >> scritto: >> >> >> >> On Apr 1, 2021, at 9:17 PM, Zhang, Hong via petsc-users < >> [email protected]> wrote: >> >> >> >> On Mar 31, 2021, at 2:53 AM, Francesco Brarda <[email protected]> >> wrote: >> >> Hi everyone! >> >> I am trying to solve a system of 3 ODEs (a basic SIR model) with TS. >> Sequentially works pretty well, but I need to switch it into a >> parallel version. >> I started working with TS not very long time ago, there are few questions >> I’d like to share with you and if you have any advices I’d be happy to hear. >> First of all, do I need to use a DM object even if the model is only time >> dependent? All the examples I found were using that object for the other >> variable when solving PDEs. >> >> >> Are you considering SIR on a spatial domain? If so, you can parallelize >> your model in the spatial domain using DM. Splitting the three variables in >> the ODE among processors would not scale. >> >> >> Even though it will not scale and will deliver slower performance it is >> completely possible for you to solve the 3 variable problem using 3 MPI >> ranks. Or 10 mpi ranks. You would just create vectors/matrices with 1 >> degree of freedom for the first three ranks and no degrees of freedom for >> the later ranks. During your function evaluation (and Jacobian evaluation) >> for TS you will need to set up the appropriate communication to get the >> values you need on each rank to evaluate the parts of the function >> evaluation needed by that rank. This is true for parallelizing any >> computation. >> >> Barry >> >> >> >> >> >> Hong (Mr.) >> >> When I preallocate the space for the Jacobian matrix, is it better to >> decide the local or global space? >> >> Best, >> Francesco >> >> >> > > -- > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which their > experiments lead. > -- Norbert Wiener > > https://www.cse.buffalo.edu/~knepley/ > <http://www.cse.buffalo.edu/~knepley/> > > > -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
