petsc-3.9.4 2018-09-11
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Documentation: FAQ






Shared Libraries


How can I subscribe to the PETSc mailing lists?


Any useful books on numerical computing?

Writing Scientific Software: A Guide to Good Style

What kind of parallel computers or clusters are needed to use PETSc? Or why do I get little speedup?

PETSc can be used with any kind of parallel system that supports MPI BUT for any decent performance one needs To obtain good performance it is important that you know your machine, how many nodes, how many memory sockets per node and how many cores per memory socket and how much memory bandwidth you obtain per core, memory socket, and node. If you do not know this and can run MPI programs with mpiexec (that is, you don't have batch system) in ${PETSC_DIR} run

make streams [NPMAX=maximum_number_of_mpi_processes_you_plan_to_use]

then it will provide a summary of the bandwidth received with different number of MPI processes and potential speedups. If you have a batch system Even if you have enough memory bandwidth if the OS switches processes between cores performance can degrade. Smart process to core/socket binding (this just means locking a process to a particular core or memory socket) may help you. For example, consider using fewer processes than cores and binding processes to separate sockets so that each process uses a different memory bus:

The software provides faster speed for ethernet systems, we have not tried it but it claims it can dramatically reduce latency and increase bandwidth on Linux system. You must first install this software and then install MPICH or Open MPI to use it.

What kind of license is PETSc released under?

See the licensing notice.

Why is PETSc programmed in C, instead of Fortran or C++?

C enables us to build data structures for storing sparse matrices, solver information, etc. in ways that Fortran simply does not allow. ANSI C is a complete standard that all modern C compilers support. The language is identical on all machines. C++ is still evolving and compilers on different machines are not identical. Using C function pointers to provide data encapsulation and polymorphism allows us to get many of the advantages of C++ without using such a large and more complicated language. It would be natural and reasonable to have coded PETSc in C++; we opted to use C instead.

Does all the PETSc error checking and logging reduce PETSc's efficiency?


How do such a small group of people manage to write and maintain such a large and marvelous package as PETSc?

  1. We work very efficiently.
    1. We use Emacs for all editing; the etags feature makes navigating and changing our source code very easy.
    2. Our manual pages are generated automatically from formatted comments in the code, thus alleviating the need for creating and maintaining manual pages.
    3. We employ automatic nightly tests of PETSc on several different machine architectures. This process helps us to discover problems the day after we have introduced them rather than weeks or months later.
  2. We are very careful in our design (and are constantly revising our design) to make the package easy to use, write, and maintain.
  3. We are willing to do the grunt work of going through all the code regularly to make sure that all code conforms to our interface design. We will never keep in a bad design decision simply because changing it will require a lot of editing; we do a lot of editing.
  4. We constantly seek out and experiment with new design ideas; we retain the useful ones and discard the rest. All of these decisions are based on practicality.
  5. Function and variable names are chosen to be very consistent throughout the software. Even the rules about capitalization are designed to make it easy to figure out the name of a particular object or routine. Our memories are terrible, so careful consistent naming puts less stress on our limited human RAM.
  6. The PETSc directory tree is carefully designed to make it easy to move throughout the entire package.
  7. Our bug reporting system, based on email to [email protected], makes it very simple to keep track of what bugs have been found and fixed. In addition, the bug report system retains an archive of all reported problems and fixes, so it is easy to refind fixes to previously discovered problems.
  8. We contain the complexity of PETSc by using object-oriented programming techniques including data encapsulation (this is why your program cannot, for example, look directly at what is inside the object Mat) and polymorphism (you call MatMult() regardless of whether your matrix is dense, sparse, parallel or sequential; you don't call a different routine for each format).
  9. We try to provide the functionality requested by our users.
  10. We never sleep.

For complex numbers will I get better performance with C++?

To use PETSc with complex numbers you either ./configure with the option --with-scalar-type=complex and either --with-clanguage=c++ or, the default, --with-clanguage=c. In our experience they will deliver very similar performance (speed), but if one is concerned they should just try both and see if one is faster.

How come when I run the same program on the same number of processes I get a "different" answer?

Inner products and norms in PETSc are computed using the MPI_Allreduce() command. In different runs the order at which values arrive at a given process (via MPI) can be in a different order, thus the order in which some floating point arithmetic operations are performed will be different. Since floating point arithmetic arithmetic is not associative, the computed quantity may be (slightly) different. Over a run the many slight differences in the inner products and norms will effect all the computed results. It is important to realize that none of the computed answers are any less right or wrong (in fact the sequential computation is no more right then the parallel ones), they are all equally valid.

The discussion above assumes that the exact same algorithm is being used on the different number of processes. When the algorithm is different for the different number of processes (almost all preconditioner algorithms except Jacobi are different for different number of processes) then one expects to see (and does) a greater difference in results for different numbers of processes. In some cases (for example block Jacobi preconditioner) it may be that the algorithm works for some number of processes and does not work for others.

How come when I run the same linear solver on a different number of processes it takes a different number of iterations?

The convergence of many of the preconditioners in PETSc including the default parallel preconditioner block Jacobi depends on the number of processes. The more processes the (slightly) slower convergence it has. This is the nature of iterative solvers, the more parallelism means the more "older" information is used in the solution process hence slower convergence.

Can PETSc use GPUs to speedup computations?

The PETSc developer repository has some support for running portions of the computation on GPUs. See PETSc GPUs for more information. PETSc has Vec classes VECCUDA and VECVIENNACL which perform almost all the vector operations on the GPU. The Mat classes AIJCUSPARSE and AIJVIENNACL perform matrix-vector products on the GPU but do not have matrix assembly on the GPU yet. Both of these classes run in parallel with MPI. All KSP methods, except KSPIBCGS, run all their vector operations on the GPU thus, for example Jacobi preconditioned Krylov methods run completely on the GPU. Preconditioners are a problem, we could do with some help for these. The example src/snes/examples/tutorials/ demonstates how the nonlinear function evaluation can be done on the GPU.

Can I run PETSc with extended precision?

Yes, with gcc 4.6 and later (and gfortran 4.6 and later) ./configure PETSc using the options --with-precision=__float128 --download-f2cblaslapack. External packages cannot be used in this mode

Why doesn't PETSc use QD to implement support for exended precision?

We tried really hard but could not. The problem is that the QD c++ classes, though they try to implement the built-in data types of double etc are not native types and cannot "just be used" in a general piece of numerical source code rather the code has to rewritten to live within the limitations of QD classes. But note, above, that PETSc can be built to use quad precision.


How do I begin using PETSc if the software has already been completely built and installed by someone else?

Assuming that the PETSc libraries have been successfully built for a particular architecture and level of optimization, a new user must merely:
  1. Set the environmental variable PETSC_DIR to the full path of the PETSc home directory (for example, /home/username/petsc).
  2. Set the environmental variable PETSC_ARCH, which indicates the configuration on which PETSc will be used. Note that the PETSC_ARCH is simply a name the installer used when installing the libraries. There many be several on a single system, like mylinux-g for the debug versions of the library and mylinux-O for the optimized version, or petscdebug for the debug version and petscopt for the optimized version.
  3. Begin by copying one of the many PETSc examples (in, for example, petsc/src/ksp/examples/tutorials) and its corresponding makefile.
  4. See the introductory section of the PETSc users manual for tips on documentation.

The PETSc distribution is SO large. How can I reduce my disk space usage?

  1. The directory ${PETSC_DIR}/docs contains a set of HTML manual pages in for use with a browser. You can delete these pages to save some disk space.
  2. The PETSc users manual is provided in PDF in ${PETSC_DIR}/docs/manual.pdf. You can delete this.
  3. The PETSc test suite contains sample output for many of the examples. These are contained in the PETSc directories ${PETSC_DIR}/src/*/examples/tutorials/output and ${PETSC_DIR}/src/*/examples/tests/output. Once you have run the test examples, you may remove all of these directories to save some disk space.
  4. The debugging versions of the libraries are larger than the optimized versions. In a pinch you can work with the optimized version although we do not recommend it generally because finding bugs is much easier with the debug version.

I want to use PETSc only for uniprocessor programs. Must I still install and use a version of MPI?

No, run ./configure with the option --with-mpi=0

Can I install PETSc to not use X windows (either under Unix or Windows with gcc, the gnu compiler)?

Yes. Run ./configure with the additional flag --with-x=0

Why do you use MPI?

MPI is the message-passing standard. Because it is a standard, it will not change over time; thus, we do not have to change PETSc every time the provider of the message-passing system decides to make an interface change. MPI was carefully designed by experts from industry, academia, and government labs to provide the highest quality performance and capability. For example, the careful design of communicators in MPI allows the easy nesting of different libraries; no other message-passing system provides this support. All of the major parallel computer vendors were involved in the design of MPI and have committed to providing quality implementations. In addition, since MPI is a standard, several different groups have already provided complete free implementations. Thus, one does not have to rely on the technical skills of one particular group to provide the message-passing libraries. Today, MPI is the only practical, portable approach to writing efficient parallel numerical software.

What do I do if my MPI compiler wrappers are invalid?

Most MPI implementations provide compiler wrappers (such as mpicc) which give the include and link options necessary to use that verson of MPI to the underlying compilers . These wrappers are either absent or broken in the MPI pointed to by --with-mpi-dir. You can rerun the configure with the additional option --with-mpi-compilers=0, which will try to auto-detect working compilers; however, these compilers may be incompatible with the particular MPI build. If this fix does not work, run with --with-cc=c_compiler where you know c_compiler works with this particular MPI, and likewise for C++ and Fortran.

When should/can I use the ./configure option --with-64-bit-indices?

By default the type that PETSc uses to index into arrays and keep sizes of arrays is a PetscInt defined to be a 32 bit int. If your problem then you need to use this option. Otherwise you will get strange crashes.

This option can be used when you are using either 32 bit or 64 bit pointers. You do not need to use this option if you are using 64 bit pointers unless the two conditions above hold.

What if I get an internal compiler error?

You can rebuild the offending file individually with a lower optimization level. Then make sure to complain to the compiler vendor and file a bug report. For example, if the compiler chokes on src/mat/impls/baij/seq/baijsolvtrannat.c you can run

make -f gmakefile PCC_FLAGS="-O1" $PETSC_ARCH/obj/src/mat/impls/baij/seq/baijsolvtrannat.o

and then invoke 'make all' again.

How do I install petsc4py with the development PETSc?

You can follow these steps
  1. install Cython
  2. install PETSc with --download-petsc4py

What Fortran compiler do you recommend for the Apple Mac OS X?

We recommend using brew ( to install gfortran

Please contact Apple at and urge them to bundle gfortran with future versions of Xcode.

How can I find the URL locations of the packages you install using --download-PACKAGE??

grep " " config/BuildSystem/config/packages/*.py

What does it mean when make test gives:

Possible error running C/C++ src/snes/examples/tutorials/ex19 with 2 MPI processes
[0]PETSC ERROR: #1 PetscOptionsInsertFile() line 563 in /Users/barrysmith/Src/PETSc/src/sys/objects/options.c
[0]PETSC ERROR: #2 PetscOptionsInsert() line 720 in /Users/barrysmith/Src/PETSc/src/sys/objects/options.c
[0]PETSC ERROR: #3 PetscInitialize() line 828 in /Users/barrysmith/Src/PETSc/src/sys/objects/pinit.c

The machine has a funky network configuration and for some reason MPICH is unable to communicate between processes with the socket connections it has established. This can happen even if you are running MPICH on just one machine. Often you will find that ping `hostname` fails with this network configuration; that is processes on the machine cannot even connect to the same machine. You can try completely disconnecting your machine from the network and see if make test then works or speaking with your system adminstrator. You can also use the ./configure options --download-mpich --download-mpich-device=ch3:nemesis


How can I redirect PETSc's stdout and stderr when programming with a GUI interface in Windows Developer Studio or too C++ streams?

To overload just the error messages write your own MyPrintError() function that does whatever you want (including pop up windows etc) and use it like below.

extern "C" {
  int PASCAL WinMain(HINSTANCE inst,HINSTANCE dumb,LPSTR param,int show);

#include <petscsys.h>
#include <mpi.h>

const char help[] = "Set up from main";

int MyPrintError(const char error[], ...) {
  printf("%s", error);
  return 0;

int main(int ac, char *av[]) {
  char buf[256];
  int i;
  PetscErrorCode ierr;

  PetscErrorPrintf = MyPrintError;

  for (i=1; i<ac; i++) {
    strcat(buf," ");

  PetscInitialize(&ac, &av, NULL, help);

  return WinMain(inst,NULL,buf,SW_SHOWNORMAL);

file in the project and compile with this preprocessor definitiions: WIN32,_DEBUG,_CONSOLE,_MBCS,USE_PETSC_LOG,USE_PETSC_BOPT_g,USE_PETSC_STA CK,_AFXDLL

And these link options: /nologo /subsystem:console /incremental:yes /debug /machine:I386 /nodefaultlib:"libcmtd.lib" /nodefaultlib:"libcd.lib" /nodefaultlib:"mvcrt.lib" /pdbtype:sept

Note that it is compiled and linked as if it was a console program. The linker will search for a main, and then from it the WinMain will start. This works with MFC templates and derived classes too.

Note: When writing a Window's console application you do not need to do anything, the stdout and stderr is automatically output to the console window.

To change where all PETSc stdout and stderr go write a function

You can also reassign PetscVFPrintf() to handle stdout and stderr any way you like write the following function:

PetscErrorCode mypetscvfprintf(FILE *fd, const char format[], va_list Argp) {
  PetscErrorCode ierr;

  if (fd != stdout && fd != stderr) { /* handle regular files */
    ierr = PetscVFPrintfDefault(fd,format,Argp); CHKERR(ierr);
  } else {
    char buff[BIG];
    int length;
    ierr = PetscVSNPrintf(buff,BIG,format,&length,Argp);CHKERRQ(ierr);

    /* now send buff to whatever stream or whatever you want */
and assign PetscVFPrintf = mypetscprintf; before PetscInitialize() in your main program.

I want to use hypre boomerAMG without GMRES but when I run -pc_type hypre -pc_hypre_type boomeramg -ksp_type preonly I don't get a very accurate answer!

You should run with -ksp_type richardson to have PETSc run several V or W cycles. -ksp_type of preonly causes boomerAMG to use only one V/W cycle. You can control how many cycles are used in a single application of the boomerAMG preconditioner with -pc_hypre_boomeramg_max_iter <it> (the default is 1). You can also control the tolerance boomerAMG uses to decide if to stop before max_iter with -pc_hypre_boomeramg_tol <tol> (the default is 1.e-7). Run with -ksp_view to see all the hypre options used and -help | grep boomeramg to see all the command line options.

You have AIJ and BAIJ matrix formats, and SBAIJ for symmetric storage, how come no SAIJ?

Just for historical reasons; the SBAIJ format with blocksize one is just as efficient as an SAIJ would be.

How do I use PETSc for Domain Decomposition?

PETSc includes Additive Schwarz methods in the suite of preconditioners. These may be activated with the runtime option -pc_type asm. Various other options may be set, including the degree of overlap -pc_asm_overlap <number> the type of restriction/extension -pc_asm_type [basic,restrict,interpolate,none] - Sets ASM type and several others. You may see the available ASM options by using -pc_type asm -help Also, see the procedural interfaces in the manual pages, with names PCASMxxxx() and check the index of the users manual for PCASMxxx().

PETSc also contains a domain decomposition inspired wirebasket or face based two level method where the coarse mesh to fine mesh interpolation is defined by solving specific local subdomain problems. It currently only works for 3D scalar problems on structured grids created with PETSc DMDAs. See the manual page for PCEXOTIC and src/ksp/ksp/examples/tutorials/ex45.c for any example.

PETSc also contains a balancing Neumann-Neumann type preconditioner, see the manual page for PCBDDC. This requires matrices be constructed with MatCreateIS() via the finite element method. See src/ksp/ksp/examples/tests/ex59.c

Can I create BAIJ matrices with different size blocks for different block rows?

Sorry, this is not possible, the BAIJ format only supports a single fixed block size on the entire matrix. But the AIJ format automatically searches for matching rows and thus still takes advantage of the natural blocks in your matrix to obtain good performance. Unfortunately you cannot use the MatSetValuesBlocked().

How do I access the values of a parallel PETSc vector on a different process than owns them?

How do I collect all the values from a parallel PETSc vector into a sequential vector on each processor?

Note that this simply concatenates in the parallel ordering of the vector. If you are using a vector from DMCreateGlobalVector() you likely want to first call DMDAGlobalToNaturalBegin/End() to scatter the original vector into the natural ordering in a new global vector before calling VecScatterBegin/End() to scatter the natural vector onto all processes.

How do I collect all the values from a parallel PETSc vector into a vector on the zeroth processor?

Note that this simply concatenates in the parallel ordering of the vector. If you are using a vector from DMCreateGlobalVector() you likely want to first call DMDAGlobalToNaturalBegin/End() to scatter the original vector into the natural ordering in a new global vector before calling VecScatterBegin/End() to scatter the natural vector onto process 0.

How can I read in or write out a sparse matrix in Matrix Market, Harwell-Boeing, SLAPC or other ASCII format?

See the examples in src/mat/examples/tests, specifically ex72.c, ex78.c, and ex32.c. You will likely need to modify the code slightly to match your required ASCII format. Note: Never read or write in parallel an ASCII matrix file, instead for reading: read in sequentially with a standalone code based on ex72.c, ex78.c, or ex32.c then save the matrix with the binary viewer PetscBinaryViewerOpen() and load the matrix in parallel in your "real" PETSc program with MatLoad(); for writing save with the binary viewer and then load with the sequential code to store it as ASCII.

Does TSSetFromOptions(), SNESSetFromOptions() or KSPSetFromOptions() reset all the parameters I previously set or how come my TS/SNES/KSPSetXXX() does not seem to work?

If XXSetFromOptions() is used (with -xxx_type aaaa) to change the type of the object then all parameters associated with the previous type are removed. Otherwise it does not reset parameters.

TS/SNES/KSPSetXXX() commands that set properties for a particular type of object (such as KSPGMRESSetRestart()) ONLY work if the object is ALREADY of that type. For example, with KSPCreate(PETSC_COMM_WORLD,&ksp); KSPGMRESSetRestart(ksp,10); the restart will be ignored since the type has not yet been set to GMRES. To have those values take effect you should do one of the following:

Can I use my own makefiles or rules for compiling code, instead of using PETSc's?

Yes, see the section of the users manual called Makefiles

Can I use CMake to build my own project that depends on PETSc?

Use the FindPETSc.cmake module from this repository. See the CMakeLists.txt from Dohp for example usage.

How can I put carriage returns in PetscPrintf() statements from Fortran?

You can use the same notation as in C, just put a \n in the string. Note that no other C format instruction is supported.

Or you can use the Fortran concatination // and char(10); for example 'some string'//char(10)//'another string on the next line'

How can I implement callbacks using C++ class methods?

Declare the class method static. Static methods do not have a this pointer, but the void* context parameter will usually be cast to a pointer to the class where it can serve the same function. Note that all PETSc callbacks return PetscErrorCode.

Everyone knows that when you code Newton's method you should compute the function and its Jacobian at the same time. How can one do this in PETSc?

The update in Newton's method is computed as u^{n+1} = u^n - lambda * approx-inverse[J(u^n)] * F(u^n)]. The reason PETSc doesn't default to computing both the function and Jacobian at the same time is

  1. In order to do the line search, F (u^n - lambda * step) may need to be computed for several lambda, the Jacobian is not needed for each of those and one does not know in advance which will be the final lambda until after the function value is computed, so many extra Jacobians may be computed.
  2. In the final step if || F(u^p)|| satisfies the convergence criteria then a Jacobian need not be computed.

You are free to have your "FormFunction" compute as much of the Jacobian at that point as you like, keep the information in the user context (the final argument to FormFunction and FormJacobian) and then retreive the information in your FormJacobian() function.

How can I use Newton's method Jacobian free? Can I difference a different function than provided with SNESSetFunction()?

The simplest way is with the option -snes_mf, this will use finite differencing of the function provided to SNESComputeFunction() to approximate the action of Jacobian. Since no matrix-representation of the Jacobian is provided the -pc_type used with this option must be -pc_type none. You can provide a custom preconditioner with SNESGetKSP(), KSPGetPC(), PCSetType(pc,PCSHELL).

The option -snes_mf_operator will use Jacobian free to apply the Jacobian (in the Krylov solvers) but will use whatever matrix you provided with SNESSetJacobian() (assuming you set one) to compute the preconditioner.

To write the code (rather than use the options above) use MatCreateSNESMF() and pass the resulting matrix object to SNESSetJacobian(). For purely matrix-free (like -snes_mf) pass the matrix object for both matrix arguments and pass the function MatMFFDComputeJacobian(). To provide your own approximate Jacobian matrix to compute the preconditioner (like -snes_mf_operator), pass this other matrix as the second matrix argument to SNESSetJacobian(). Make sure your provided computejacobian() function calls MatAssemblyBegin/End() separately on BOTH matrix arguments to this function. See src/snes/examples/tests/ex7.c

To difference a different function than that passed to SNESSetJacobian() to compute the matrix-free Jacobian multiply call MatMFFDSetFunction() to set that other function. See src/snes/examples/tests/ex7.c.h

How can I determine the condition number of a matrix?

For small matrices, the condition number can be reliably computed using -pc_type svd -pc_svd_monitor. For larger matrices, you can run with -pc_type none -ksp_type gmres -ksp_monitor_singular_value -ksp_gmres_restart 1000 to get approximations to the condition number of the operator. This will generally be accurate for the largest singular values, but may overestimate the smallest singular value unless the method has converged. Make sure to avoid restarts. To estimate the condition number of the preconditioned operator, use -pc_type somepc in the last command.

How can I compute the inverse of a matrix in PETSc?

It is very expensive to compute the inverse of a matrix and very rarely needed in practice. We highly recommend avoiding algorithms that need it. The inverse of a matrix (dense or sparse) is essentially always dense, so begin by creating a dense matrix B and fill it with the identity matrix (ones along the diagonal), also create a dense matrix X of the same size that will hold the solution. Then factor the matrix you wish to invert with MatLUFactor() or MatCholeskyFactor(), call the result A. Then call MatMatSolve(A,B,X) to compute the inverse into X. See also.

How can I compute the Schur complement, Kbb - Kab * inverse(Kbb) * Kba in PETSc?

It is very expensive to compute the Schur complement of a matrix and very rarely needed in practice. We highly recommend avoiding algorithms that need it. The Schur complement of a matrix (dense or sparse) is essentially always dense, so begin by

For computing Schur complements like this it does not make sense to use the KSP iterative solvers since for solving many moderate size problems using a direct factorization is much faster than iterative solvers. As you can see, this requires a great deal of work space and computation so is best avoided. However, it is not necessary to assemble the Schur complement S in order to solve systems with it. Use MatCreateSchurComplement(Kaa,Kaa_pre,Kab,Kba,Kbb,&S) to create a matrix that applies the action of S (using Kaa_pre to solve with Kaa), but does not assemble. Alternatively, if you already have a block matrix K = [Kaa, Kab; Kba, Kbb] (in some ordering), then you can create index sets (IS) isa and isb to address each block, then use MatGetSchurComplement() to create the Schur complement and/or an approximation suitable for preconditioning. Since S is generally dense, standard preconditioning methods cannot typically be applied directly to Schur complements. There are many approaches to preconditioning Schur complements including using the SIMPLE approximation K_bb - Kba inv(diag(Kaa)) Kab to create a sparse matrix that approximates the Schur complement (this is returned by default for the optional "preconditioning" matrix in MatGetSchurComplement()). An alternative is to interpret the matrices as differential operators and apply approximate commutator arguments to find a spectrally equivalent operation that can be applied efficiently (see the "PCD" preconditioners from Elman, Silvester, and Wathen). A variant of this is the least squares commutator, which is closely related to the Moore-Penrose pseudoinverse, and is available in PCLSC which operates on matrices of type MATSCHURCOMPLEMENT.

Do you have examples of doing unstructured grid finite element computations (FEM) with PETSc?

There are at least two ways to write a finite element code using PETSc
  1. use DMPlex, which is a high level approach to manage your mesh and discretization. SNES ex62 solves the Stokes equation using this approach.
  2. manage the grid data structure yourself and use PETSc IS and VecScatter to communicate the required ghost point communication. See src/snes/examples/tutorials/ex10d/ex10.c

The PETSc DA object decomposes the domain differently than the MPI_Cart_create() command. How can one use them together?

The MPI_Cart_create() first divides the mesh along the z direction, then the y, then the x. DMDA divides along the x, then y, then z. Thus, for example, rank 1 of the processes will be in a different part of the mesh for the two schemes. To resolve this you can create a new MPI communicator that you pass to DMDACreate() that renumbers the process ranks so that each physical process shares the same part of the mesh with both the DMDA and the MPI_Cart_create(). The code to determine the new numbering was provided by Rolf Kuiper.

// the numbers of processors per direction are (int) x_procs, y_procs, z_procs respectively
// (no parallelization in direction 'dir' means dir_procs = 1)

MPI_Comm NewComm;
int MPI_Rank, NewRank, x,y,z;

// get rank from MPI ordering:
MPI_Comm_rank(MPI_COMM_WORLD, &MPI_Rank);

// calculate coordinates of cpus in MPI ordering:
x = MPI_rank / (z_procs*y_procs);
y = (MPI_rank % (z_procs*y_procs)) / z_procs;
z = (MPI_rank % (z_procs*y_procs)) % z_procs;

// set new rank according to PETSc ordering:
NewRank = z*y_procs*x_procs + y*x_procs + x;

// create communicator with new ranks according to
PETSc ordering:
MPI_Comm_split(PETSC_COMM_WORLD, 1, NewRank, &NewComm);

// override the default communicator (was
MPI_COMM_WORLD as default)

When solving a system with Dirichlet boundary conditions I can use MatZeroRows() to eliminate the Dirichlet rows but this results in a non-symmetric system. How can I apply Dirichlet boundary conditions but keep the matrix symmetric?

For nonsymmetric systems put the appropriate boundary solutions in the x vector and use MatZeroRows() followed by KSPSetOperators(). For symmetric problems use MatZeroRowsColumns() instead. If you have many Dirichlet locations you can use MatZeroRows() (not MatZeroRowsColumns()) and -ksp_type preonly -pc_type redistribute; see PCREDISTRIBUTE) and PETSc will repartition the parallel matrix for load balancing; in this case the new matrix solved remains symmetric even though MatZeroRows() is used.

An alternative approach is, when assemblying the matrix (generating values and passing them to the matrix), never to include locations for the Dirichlet grid points in the vector and matrix, instead taking them into account as you put the other values into the load.

How can I get PETSc Vecs and Mats to MATLAB or vice versa?

There are five ways to work with PETSc and MATLAB
  1. Using the MATLAB Engine, allowing PETSc to automatically call MATLAB to perform some specific computations. This does not allow MATLAB to be used interactively by the user. See the PetscMatlabEngine.
  2. To save PETSc Mats and Vecs to files that can be read from MATLAB use PetscViewerBinaryOpen() viewer and VecView() or MatView() to save objects for MATLAB and VecLoad() and MatLoad() to get the objects that MATLAB has saved. See PetscBinaryRead.m and PetscBinaryWrite.m in share/petsc/matlab for loading and saving the objects in MATLAB.
  3. You can open a socket connection between MATLAB and PETSc to allow sending objects back and forth between an interactive MATLAB session and a running PETSc program. See PetscViewerSocketOpen() for access from the PETSc side and PetscReadBinary.m in share/petsc/matlab for access from the MATLAB side.
  4. You can save PETSc Vecs (not Mats) with the PetscViewerMatlabOpen() viewer that saves .mat files can then be loaded into MATLAB.
  5. We are just beginning to develop in petsc master (branch in git) an API to call most of the PETSc function directly from MATLAB; we could use help in developing this. See share/petsc/matlab/classes/PetscInitialize.m

How do I get started with Cython so that I can extend petsc4py?

Steps I used:
  1. Learn how to build a Cython module
  2. Go through the simple example provided by Denis here. Note also the next comment that shows how to create numpy arrays in the Cython and pass them back.
  3. Check out this page which tells you how to get fast indexing
  4. Have a look at the petsc4py array source

I would like to compute a custom norm for KSP to use as a convergence test or for monitoring?

You need to call KSPBuildResidual() on your KSP object and then compute the appropriate norm on the resulting residual. Note that depending on the KSPSetNormType() of the method you may not return the same norm as provided by the method. See also KSPSetPCSide()

If I have a sequential program can I use a parallel direct solver?

Yes, you must ./configure PETSc with MPI (even though you will not use MPI) and the options --download-superlu_dist --download-parmetis --download-metis --with-openmp

Your compiler must support OpenMP. To have the linear solver run in parallel run your program with

OMP_NUM_THREADS=n ./programname -pc_type lu -pc_factor_mat_solver superlu_dist

where n is the number of threads and should be less than or equal to the number of cores available. Do not expect to get great speedups but you should see some reduction in the time for the linear solvers.

If your code is MPI parallel you can also use these same options to have SuperLU_dist utilize multiple threads per MPI process for the direct solver. Make sure that the OMP_NUM_THREADS you use per MPI process is less than or equal to the number of cores available for each MPI process. For example if your compute nodes have 6 cores and you use 2 MPI processes per node then set OMP_NUM_THREADS to 2 or 3.


PETSc executables are SO big and take SO long to link.

We find this annoying as well. On most machines PETSc can use shared libraries, so executables should be much smaller, run ./configure with the additional option --with-shared-libraries (this is the default). Also, if you have room, compiling and linking PETSc on your machine's /tmp disk or similar local disk, rather than over the network will be much faster.

PETSc has so many options for my program that it is hard to keep them straight.

Running the PETSc program with the option -help will print of many of the options. To print the options that have been specified within a program, employ -options_left to print any options that the user specified but were not actually used by the program and all options used; this is helpful for detecting typo errors.

PETSc automatically handles many of the details in parallel PDE solvers. How can I understand what is really happening within my program?

You can use the option -info to get more details about the solution process. The option -log_view provides details about the distribution of time spent in the various phases of the solution process. You can run with -ts_view or -snes_view or -ksp_view to see what solver options are being used. Run with -ts_monitor -snes_monitor or -ksp_monitor to watch convergence of the methods. -snes_converged_reason and -ksp_converged_reason will indicate why and if the solvers have converged.

Assembling large sparse matrices takes a long time. What can I do make this process faster? or MatSetValues() is so slow, what can I do to speed it up?

See the Performance chapter of the users manual for many tips on this.
  1. Preallocate enough space for the sparse matrix. For example, rather than calling MatCreateSeqAIJ(comm,n,n,0,NULL,&mat); call MatCreateSeqAIJ(comm,n,n,rowmax,NULL,&mat); where rowmax is the maximum number of nonzeros expected per row. Or if you know the number of nonzeros per row, you can pass this information in instead of the NULL argument. See the manual pages for each of the MatCreateXXX() routines.
  2. Insert blocks of values into the matrix, rather than individual components.

Preallocation of matrix memory is crucial for good performance for large problems, see:

If you can set several nonzeros in a block at the same time, this is faster than calling MatSetValues() for each individual matrix entry.

It is best to generate most matrix entries on the process they belong to (so they do not have to be stashed and then shipped to the owning process). Note: it is fine to have some entries generated on the "wrong" process, just not many.

How can I generate performance summaries with PETSc?

Use these options at runtime: -log_view. See the Performance chapter of the users manual for information on interpreting the summary data. If using the PETSc (non)linear solvers, one can also specify -snes_view or -ksp_view for a printout of solver info. Only the highest level PETSc object used needs to specify the view option.

Why do I get different answers on a different numbers of processors?

Most commonly, you are using a preconditioner which behaves differently based upon the number of processors, such as Block-Jacobi which is the PETSc default. However, since computations are reordered in parallel, small roundoff errors will still be present with identical mathematical formulations. If you set a tighter linear solver tolerance (using -ksp_rtol), the differences will decrease.

How do I know the amount of time spent on each level of the multigrid solver/preconditioner?

Run with -log_view and -pc_mg_log

Where do I get the input matrices for the examples?

Some examples use ${DATAFILESPATH}/matrices/medium and other files. These test matrices in PETSc binary format can be found with anonymous ftp from in the directory pub/petsc/Datafiles/matrices. The are not included with the PETSc distribution in the interest of reducing the distribution size. You can obtain many of the files in pub/petsc/Datafiles.tar.gz

When I dump some matrices and vectors to binary, I seem to be generating some empty files with .info extensions. What's the deal with these?

PETSc binary viewers put some additional information into .info files like matrix block size; it is harmless but if you really don't like it you can use -viewer_binary_skip_info or PetscViewerBinarySkipInfo() note you need to call PetscViewerBinarySkipInfo() before PetscViewerFileSetName(). In other words you cannot use PetscViewerBinaryOpen() directly.

Why is my parallel solver slower than my sequential solver, or I have poor speed-up?

This can happen for many reasons:

What steps are necessary to make the pipelined solvers execute efficiently?

Pipelined solvers like KSPPGMRES, KSPPIPECG, KSPPIPECR, and KSPGROPPCG may require special MPI configuration to effectively overlap reductions with computation. In general, this requires an MPI-3 implementation, an implementation that supports multiple threads, and use of a "progress thread". Consult the documentation from your vendor or computing facility for more.
MPICH version 3.0 and later implements the MPI-3 standard and the default configuration supports use of threads. Use of a progress thread is configured by setting the environment variable MPICH_ASYNC_PROGRESS=1.
Cray MPI
Cray MPT-5.6 supports MPI-3, but requires the environment variable MPICH_MAX_THREAD_SAFETY=multiple for threads, plus either MPICH_ASYNC_PROGRESS=1 or MPICH_NEMESIS_ASYNC_PROGRESS=1.

When using PETSc in single precision mode (--with-precision=single when running ./configure) are the operations done in single or double precision?

PETSc does NOT do any explicit conversion of single precision to double before performing computations; this it depends on the hardware and compiler what happens. For example, the compiler could choose to put the single precision numbers into the usual double precision registers and then use the usual double precision floating point unit. Or it could use SSE2 instructions that work directly on the single precision numbers. It is a bit of a mystery what decisions get made sometimes. There may be compiler flags in some circumstances that can affect this.

Why is Newton's method (SNES) not converging, or converges slowly?

Newton's method may not converge for many reasons, here are some of the most common. Here are some of the ways to help debug lack of convergence of Newton. Here are some ways to help the Newton process if everything above checks out

Why is the linear solver (KSP) not converging, or converges slowly?

Always run with -ksp_converged_reason -ksp_monitor_true_residual when trying to learn why a method is not converging. Common reasons for KSP not converging are


How do I turn off PETSc signal handling so I can use the -C option on xlF?

Immediately after calling PetscInitialize() call PetscPopSignalHandler()

Some Fortran compilers including the IBM xlf, xlF etc compilers have a compile option (-C for IBM's) that causes all array access in Fortran to be checked that they are in-bounds. This is a great feature but does require that the array dimensions be set explicitly, not with a *.

How do I debug if -start_in_debugger does not work on my machine?

On newer Mac OSX machines - one has to be in admin group to be able to use debugger

On newer UBUNTU linux machines - one has to disable ptrace_scop with "sudo echo 0 > /proc/sys/kernel/yama/ptrace_scope" - to get start in debugger working.

If start_in_debugger does not really work on your OS, for a uniprocessor job, just try the debugger directly, for example: gdb ex1. You can also use Totalview which is a good graphical parallel debugger.

How do I see where my code is hanging?

You can use the -start_in_debugger option to start all processes in the debugger (each will come up in its own xterm) or run in Totalview. Then use cont (for continue) in each xterm. Once you are sure that the program is hanging, hit control-c in each xterm and then use 'where' to print a stack trace for each process.

How can I inspect Vec and Mat values when in the debugger?

I will illustrate this with gdb, but it should be similar on other debuggers. You can look at local Vec values directly by obtaining the array. For a Vec v, we can print all local values using

(gdb) p ((Vec_Seq*) v->data)->array[0]@v->map.n

However, this becomes much more complicated for a matrix. Therefore, it is advisable to use the default viewer to look at the object. For a Vec v and a Mat m, this would be

  (gdb) call VecView(v, 0)
  (gdb) call MatView(m, 0)

or with a communicator other than MPI_COMM_WORLD,

(gdb) call MatView(m, PETSC_VIEWER_STDOUT_(m->comm))

Totalview 8.8.0 has a new feature that allows libraries to provide their own code to display objects in the debugger. Thus in theory each PETSc object, Vec, Mat etc could have custom code to print values in the object. We have only done this for the most elementary display of Vec and Mat. See the routine TV_display_type() in src/vec/vec/interface/vector.c for an example of how these may be written. Contact us if you would like to add more.

How can I find the cause of floating point exceptions like not-a-number (NaN) or infinity?

The best way to locate floating point exceptions is to use a debugger. On supported architectures (including Linux and glibc-based systems), just run in a debugger and pass -fp_trap to the PETSc application. This will activate signaling exceptions and the debugger will break on the line that first divides by zero or otherwise generates an exceptions. Without a debugger, running with -fp_trap in debug mode will only identify the function in which the error occurred, but not the line or the type of exception. If -fp_trap is not supported on your architecture, consult the documentation for your debugger since there is likely a way to have it catch exceptions.

Error while loading shared libraries: cannot open shared object file: No such file or directory.

The Intel compilers use shared libraries (like libimf) that cannot by default at run time. When using the Intel compilers (and running the resulting code) you must make sure that the proper Intel initialization scripts are run. This is usually done by putting some code into your .cshrc, .bashrc, .profile etc file. Sometimes on batch file systems that do now access your initialization files (like .cshrc) you must include the initialization calls in your batch file submission.

For example, on my Mac using csh I have the following in my .cshrc file
source /opt/intel/cc/10.1.012/bin/iccvars.csh
source /opt/intel/fc/10.1.012/bin/ifortvars.csh
source /opt/intel/idb/10.1.012/bin/idbvars.csh
in my .profile I have
source /opt/intel/cc/10.1.012/bin/
source /opt/intel/fc/10.1.012/bin/
source /opt/intel/idb/10.1.012/bin/

What does Object Type not set: Argument # n mean?

Many operations on PETSc objects require that the specific type of the object be set before the operations is performed. You must call XXXSetType() or XXXSetFromOptions() before you make the offending call. For example, MatCreate(comm,&A); MatSetValues(A,....); will not work. You must add MatSetType(A,...) or MatSetFromOptions(A,....); before the call to MatSetValues();

What does Error detected in PetscSplitOwnership() about "sum of local lengths ...": mean?

In a previous call to VecSetSizes(), MatSetSizes(), VecCreateXXX() or MatCreateXXX() you passed in local and global sizes that do not make sense for the correct number of processors. For example if you pass in a local size of 2 and a global size of 100 and run on two processors, this cannot work since the sum of the local sizes is 4, not 100.

What does Corrupt argument or Caught signal or SEQV or segmentation violation or bus error mean? Can I use valgrind to debug memory corruption issues?

Sometimes it can mean an argument to a function is invalid. In Fortran this may be caused by forgeting to list an argument in the call, especially the final ierr.

Otherwise it is usually caused by memory corruption; that is somewhere the code is writing out of array bounds. To track this down rerun the debug version of the code with the option -malloc_debug. Occasionally the code may crash only with the optimized version, in that case run the optimized version with -malloc_debug. If you determine the problem is from memory corruption you can put the macro CHKMEMQ in the code near the crash to determine exactly what line is causing the problem.

If -malloc_debug does not help: on GNU/Linux and Apple Mac OS X machines - you can try using to look for memory corruption. - Make sure valgrind is installed Notes:

What does Detected zero pivot in LU factorization mean?

A zero pivot in LU, ILU, Cholesky, or ICC sparse factorization does not always mean that the matrix is singular. You can use '-pc_factor_shift_type NONZERO -pc_factor_shift_amount [amount]' or '-pc_factor_shift_type POSITIVE_DEFINITE'; '-[level]_pc_factor_shift_type NONZERO -pc_factor_shift_amount [amount]' or '-[level]_pc_factor_shift_type POSITIVE_DEFINITE' to prevent the zero pivot. [level] is "sub" when lu, ilu, cholesky, or icc are employed in each individual block of the bjacobi or ASM preconditioner; and [level] is "mg_levels" or "mg_coarse" when lu, ilu, cholesky, or icc are used inside multigrid smoothers or to the coarse grid solver. See PCFactorSetShiftType(), PCFactorSetAmount().

This error can also happen if your matrix is singular, see MatSetNullSpace() for how to handle this.

If this error occurs in the zeroth row of the matrix, it is likely you have an error in the code that generates the matrix.

You create Draw windows or ViewerDraw windows or use options -ksp_monitor_lg_residualnorm or -snes_monitor_lg_residualnorm and the program seems to run OK but windows never open.

The libraries were compiled without support for X windows. Make sure that ./configure was run with the option --with-x

The program seems to use more and more memory as it runs, even though you don't think you are allocating more memory.

Problem: Possibly some of the following:
  1. You are creating new PETSc objects but never freeing them.
  2. There is a memory leak in PETSc or your code.
  3. Something much more subtle: (if you are using Fortran). When you declare a large array in Fortran, the operating system does not allocate all the memory pages for that array until you start using the different locations in the array. Thus, in a code, if at each step you start using later values in the array your virtual memory usage will "continue" to increase as measured by ps or top.
  4. You are running with the -log, -log_mpe, or -log_all option. With these options, a great deal of logging information is stored in memory until the conclusion of the run.
  5. You are linking with the MPI profiling libraries; these cause logging of all MPI activities. Another Symptom is at the conclusion of the run it may print some message about writing log files.
  1. Run with the -malloc_debug option and -malloc_dump. Or use the commands PetscMallocDump() and PetscMallocLogDump() sprinkled in your code to track memory that is allocated and not later freed. Use the commands PetscMallocGetCurrentUsage() and PetscMemoryGetCurrentUsage() to monitor memory allocated and PetscMallocGetMaximumUsage() and PetscMemoryGetMaximumUsage() for total memory used as the code progresses.
  2. This is just the way Unix works and is harmless.
  3. Do not use the -log, -log_mpe, or -log_all option, or use PLogEventDeactivate() or PLogEventDeactivateClass() to turn off logging of specific events.
  4. Make sure you do not link with the MPI profiling libraries.

When calling MatPartitioningApply() you get a message Error! Key 16615 not found

The graph of the matrix you are using is not symmetric. You must use symmetric matrices for partitioning.

With GMRES At restart the second residual norm printed does not match the first.

26 KSP Residual norm 3.421544615851e-04
27 KSP Residual norm 2.973675659493e-04
28 KSP Residual norm 2.588642948270e-04
29 KSP Residual norm 2.268190747349e-04
30 KSP Residual norm 1.977245964368e-04
30 KSP Residual norm 1.994426291979e-04 <----- At restart the residual norm is printed a second time

Problem: Actually this is not surprising. GMRES computes the norm of the residual at each iteration via a recurrence relation between the norms of the residuals at the previous iterations and quantities computed at the current iteration; it does not compute it via directly || b - A x^{n} ||. Sometimes, especially with an ill-conditioned matrix, or computation of the matrix-vector product via differencing, the residual norms computed by GMRES start to "drift" from the correct values. At the restart, we compute the residual norm directly, hence the "strange stuff," the difference printed. The drifting, if it remains small, is harmless (doesn't affect the accuracy of the solution that GMRES computes).

Cure: There realy isn't a cure, but if you use a more powerful preconditioner the drift will often be smaller and less noticeable. Of if you are running matrix-free you may need to tune the matrix-free parameters.

Why do some Krylov methods seem to print two residual norms per iteration?

1198 KSP Residual norm 1.366052062216e-04
1198 KSP Residual norm 1.931875025549e-04
1199 KSP Residual norm 1.366026406067e-04
1199 KSP Residual norm 1.931819426344e-04

Some Krylov methods, for example tfqmr, actually have a "sub-iteration" of size 2 inside the loop; each of the two substeps has its own matrix vector product and application of the preconditioner and updates the residual approximations. This is why you get this "funny" output where it looks like there are two residual norms per iteration. You can also think of it as twice as many iterations.

Unable to locate PETSc dynamic library /home/balay/spetsc/lib/libg/linux/libpetsc

When using DYNAMIC libraries - the libraries cannot be moved after they are installed. This could also happen on clusters - where the paths are different on the (run) nodes - than on the (compile) front-end. Do not use dynamic libraries & shared libraries. Run ./configure with --with-shared-libraries=0 --with-dynamic-loading=0. This option has been removed in petsc-3.5.

How do I determine what update to PETSc broke my code?

If at some point [in petsc code history] you had a working code - but the latest petsc code broke it, its possible to determine the petsc code change that might have caused this behavior. This is achieved by: This process can be as follows:

Shared Libraries

Can I install PETSc libraries as shared libraries?

Yes.Use the ./configure --with-shared-libraries

Why should I use shared libraries?

When you link to shared libraries, the function symbols from the shared libraries are not copied in the executable. This way the size of the executable is considerably smaller than when using regular libraries. This helps in a couple of ways:
  1. saves disk space when more than one executable is created, and
  2. improves the compile time immensly, because the compiler has to write a much smaller file (executable) to the disk.

How do I link to the PETSc shared libraries?

By default, the compiler should pick up the shared libraries instead of the regular ones. Nothing special should be done for this.

What If I want to link to the regular .a library files?

You must run ./configure with the option --with-shared-libraries=0 (you can use a different PETSC_ARCH for this build so you can easily switch between the two).

What do I do if I want to move my executable to a different machine?

You would also need to have access to the shared libraries on this new machine. The other alternative is to build the exeutable without shared libraries by first deleting the shared libraries, and then creating the executable.