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?
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] > <mailto:[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] >> <mailto:[email protected]>> ha scritto: >> >> >> >>> On Apr 1, 2021, at 9:17 PM, Zhang, Hong via petsc-users >>> <[email protected] <mailto:[email protected]>> wrote: >>> >>> >>> >>>> On Mar 31, 2021, at 2:53 AM, Francesco Brarda <[email protected] >>>> <mailto:[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/>
