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# Profiling

PETSc includes a consistent, lightweight scheme for profiling application programs. The PETSc routines automatically log performance data if certain options are specified at runtime. The user can also log information about application codes for a complete picture of performance.

In addition, as described in Interpreting -log_view Output: The Basics, PETSc provides a mechanism for printing informative messages about computations. Basic Profiling Information introduces the various profiling options in PETSc, while the remainder of the chapter focuses on details such as monitoring application codes and tips for accurate profiling.

## Basic Profiling Information

The profiling options include the following:

- `-log_view [:filename]` - Prints an ASCII version of performance data at the program’s conclusion. These statistics are comprehensive and concise and require little overhead; thus, `-log_view` is intended as the primary means of monitoring the performance of PETSc codes. See Interpreting -log_view Output: The Basics

- `-info [infofile]` - Prints verbose information about code to stdout or an optional file. This option provides details about algorithms, data structures, etc. Since the overhead of printing such output slows a code, this option should not be used when evaluating a program’s performance. See Interpreting -info Output: Informative Messages

- `-log_trace [logfile]` - Traces the beginning and ending of all PETSc events. This option, which can be used in conjunction with `-info`, is useful to see where a program is hanging without running in the debugger. See `PetscLogTraceBegin()`.

As discussed in Using -log_mpe with Jumpshot, additional profiling can be done with MPE.

### Interpreting `-log_view` Output: The Basics

As shown in listing in Profiling Programs, the option `-log_view` `[:filename]` activates printing of profile data to standard output or an ASCII file at the conclusion of a program. See `PetscLogView()` for all the possible output options.

We print performance data for each routine, organized by PETSc libraries, followed by any user-defined events (discussed in Profiling Application Codes). For each routine, the output data include the maximum time and floating point operation (flop) rate over all processes. Information about parallel performance is also included, as discussed in the following section.

For the purpose of PETSc floating point operation counting, we define one flop as one operation of any of the following types: multiplication, division, addition, or subtraction. For example, one `VecAXPY()` operation, which computes \(y = \alpha x + y\) for vectors of length \(N\), requires \(2N\) flop (consisting of \(N\) additions and \(N\) multiplications). Bear in mind that flop rates present only a limited view of performance, since memory loads and stores are the real performance barrier.

For simplicity, the remainder of this discussion focuses on interpreting profile data for the `KSP` library, which provides the linear solvers at the heart of the PETSc package. Recall the hierarchical organization of the PETSc library, as shown in Numerical Libraries in PETSc. Each `KSP` solver is composed of a `PC` (preconditioner) and a `KSP` (Krylov subspace) part, which are in turn built on top of the `Mat` (matrix) and `Vec` (vector) modules. Thus, operations in the `KSP` module are composed of lower-level operations in these packages. Note also that the nonlinear solvers library, `SNES`, is built on top of the `KSP` module, and the timestepping library, `TS`, is in turn built on top of `SNES`.

We briefly discuss interpretation of the sample output in listing, which was generated by solving a linear system on one process using restarted GMRES and ILU preconditioning. The linear solvers in `KSP` consist of two basic phases, `KSPSetUp()` and `KSPSolve()`, each of which consists of a variety of actions, depending on the particular solution technique. For the case of using the `PCILU` preconditioner and `KSPGMRES` Krylov subspace method, the breakdown of PETSc routines is listed below. As indicated by the levels of indentation, the operations in `KSPSetUp()` include all of the operations within `PCSetUp()`, which in turn include `MatILUFactor()`, and so on.

- `KSPSetUp` - Set up linear solver

  - `PCSetUp` - Set up preconditioner

    - `MatILUFactor` - Factor matrix

      - `MatILUFactorSymbolic` - Symbolic factorization phase

      - `MatLUFactorNumeric` - Numeric factorization phase

- `KSPSolve` - Solve linear system

  - `PCApply` - Apply preconditioner

    - `MatSolve` - Forward/backward triangular solves

  - `KSPGMRESOrthog` - Orthogonalization in GMRES

    - `VecDot` or `VecMDot` - Inner products

    - `VecAXPY` or `VecMAXPY` - vector updates

  - `MatMult` - Matrix-vector product

  - `MatMultAdd` - Matrix-vector product + vector addition

    - `VecScale`, `VecNorm`, `VecAXPY`, `VecCopy`, …

The summaries printed via `-log_view` reflect this routine hierarchy. For example, the performance summaries for a particular high-level routine such as `KSPSolve()` include all of the operations accumulated in the lower-level components that make up the routine.

The output produced with `-log_view` is flat, meaning that the hierarchy of PETSc operations is not completely clear. For a particular problem, the user should generally have an idea of the basic operations that are required for its implementation (e.g., which operations are performed when using GMRES and ILU, as described above), so that interpreting the `-log_view` data should be relatively straightforward. If this is problematic then it is also possible to examine the profiling information in a nested format. For more information see Profiling Nested Events.

### Interpreting `-log_view` Output: Parallel Performance

We next discuss performance summaries for parallel programs, as shown within listing and listing, which presents the output generated by the `-log_view` option. The program that generated this data is KSP Tutorial ex10. The code loads a matrix and right-hand-side vector from a binary file and then solves the resulting linear system; the program then repeats this process for a second linear system. This particular case was run on four processors of an Intel x86_64 Linux cluster, using restarted GMRES and the block Jacobi preconditioner, where each block was solved with ILU. The two input files `medium` and `arco6` can be obtained from datafiles, see Other PETSc repositories.

The first listing presents an overall performance summary, including times, floating-point operations, computational rates, and message-passing activity (such as the number and size of messages sent and collective operations). Summaries for various user-defined stages of monitoring (as discussed in Profiling Multiple Sections of Code) are also given. Information about the various phases of computation then follow (as shown separately here in the second listing). Finally, a summary of object creation and destruction is presented.

    mpiexec -n 4 ./ex10 -f0 medium -f1 arco6 -ksp_gmres_classicalgramschmidt -log_view -mat_type baij \
                -matload_block_size 3 -pc_type bjacobi -options_left

    Number of iterations = 19
    Residual norm 1.088292e-05
    Number of iterations = 59
    Residual norm 3.871022e-02
    ---------------------------------------------- PETSc Performance Summary: ----------------------------------------------

    ./ex10 on a intel-bdw-opt named beboplogin4 with 4 processors, by jczhang Mon Apr 23 13:36:54 2018
    Using PETSc Development Git Revision: v3.9-163-gbe3efd42 Git Date: 2018-04-16 10:45:40 -0500

                             Max       Max/Min        Avg      Total
    Time (sec):           1.849e-01      1.00002   1.849e-01
    Objects:              1.060e+02      1.00000   1.060e+02
    Flops:                2.361e+08      1.00684   2.353e+08  9.413e+08
    Flops/sec:            1.277e+09      1.00685   1.273e+09  5.091e+09
    MPI Msg Count:        2.360e+02      1.34857   2.061e+02  8.245e+02
    MPI Msg Len (bytes):  1.256e+07      2.24620   4.071e+04  3.357e+07
    MPI Reductions:       2.160e+02      1.00000

    Summary of Stages:   ----- Time ------  ----- Flop -----  --- Messages ---  -- Message Lengths --  -- Reductions --
                            Avg     %Total     Avg     %Total   counts   %Total     Avg         %Total   counts   %Total
     0:      Main Stage: 5.9897e-04   0.3%  0.0000e+00   0.0%  0.000e+00   0.0%  0.000e+00        0.0%  2.000e+00   0.9%
     1:   Load System 0: 2.9113e-03   1.6%  0.0000e+00   0.0%  3.550e+01   4.3%  5.984e+02        0.1%  2.200e+01  10.2%
     2:      KSPSetUp 0: 7.7349e-04   0.4%  9.9360e+03   0.0%  0.000e+00   0.0%  0.000e+00        0.0%  2.000e+00   0.9%
     3:      KSPSolve 0: 1.7690e-03   1.0%  2.9673e+05   0.0%  1.520e+02  18.4%  1.800e+02        0.1%  3.900e+01  18.1%
     4:   Load System 1: 1.0056e-01  54.4%  0.0000e+00   0.0%  3.700e+01   4.5%  5.657e+05       62.4%  2.200e+01  10.2%
     5:      KSPSetUp 1: 5.6883e-03   3.1%  2.1205e+07   2.3%  0.000e+00   0.0%  0.000e+00        0.0%  2.000e+00   0.9%
     6:      KSPSolve 1: 7.2578e-02  39.3%  9.1979e+08  97.7%  6.000e+02  72.8%  2.098e+04       37.5%  1.200e+02  55.6%

    ------------------------------------------------------------------------------------------------------------------------

    .... [Summary of various phases, see part II below] ...

    ------------------------------------------------------------------------------------------------------------------------

    Object Type          Creations   Destructions    (Reports information only for process 0.)
    ...
    --- Event Stage 3: KSPSolve 0

                  Matrix     0              4
                  Vector    20             30
               Index Set     0              3
             Vec Scatter     0              1
           Krylov Solver     0              2
          Preconditioner     0              2

We next focus on the summaries for the various phases of the computation, as given in the table within the following listing. The summary for each phase presents the maximum times and flop rates over all processes, as well as the ratio of maximum to minimum times and flop rates for all processes. A ratio of approximately 1 indicates that computations within a given phase are well balanced among the processes; as the ratio increases, the balance becomes increasingly poor. Also, the total computational rate (in units of MFlop/sec) is given for each phase in the final column of the phase summary table.

\[ {\rm Total\: Mflop/sec} \:=\: 10^{-6} * ({\rm sum\; of\; flop\; over\; all\; processors})/({\rm max\; time\; over\; all\; processors}) \]

Note: Total computational rates \(<\) 1 MFlop are listed as 0 in this column of the phase summary table. Additional statistics for each phase include the total number of messages sent, the average message length, and the number of global reductions.

    mpiexec -n 4 ./ex10 -f0 medium -f1 arco6 -ksp_gmres_classicalgramschmidt -log_view -mat_type baij \
                -matload_block_size 3 -pc_type bjacobi -options_left

    ---------------------------------------------- PETSc Performance Summary: ----------------------------------------------
    .... [Overall summary, see part I] ...

    Phase summary info:
       Count: number of times phase was executed
       Time and Flop/sec: Max - maximum over all processors
                           Ratio - ratio of maximum to minimum over all processors
       Mess: number of messages sent
       AvgLen: average message length
       Reduct: number of global reductions
       Global: entire computation
       Stage: optional user-defined stages of a computation. Set stages with PetscLogStagePush() and PetscLogStagePop().
          %T - percent time in this phase         %F - percent flop in this phase
          %M - percent messages in this phase     %L - percent message lengths in this phase
          %R - percent reductions in this phase
       Total Mflop/s: 10^6 * (sum of flop over all processors)/(max time over all processors)
    ------------------------------------------------------------------------------------------------------------------------
    Phase              Count      Time (sec)       Flop/sec                          --- Global ---  --- Stage ----  Total
                                Max    Ratio      Max    Ratio  Mess AvgLen  Reduct  %T %F %M %L %R  %T %F %M %L %R Mflop/s
    ------------------------------------------------------------------------------------------------------------------------
    ...

    --- Event Stage 5: KSPSetUp 1

    MatLUFactorNum         1 1.0 3.6440e-03 1.1 5.30e+06 1.0 0.0e+00 0.0e+00 0.0e+00  2  2  0  0  0  62100  0  0  0  5819
    MatILUFactorSym        1 1.0 1.7111e-03 1.4 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  1  0  0  0  0  26  0  0  0  0     0
    MatGetRowIJ            1 1.0 1.1921e-06 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0
    MatGetOrdering         1 1.0 3.0041e-05 1.1 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   1  0  0  0  0     0
    KSPSetUp               2 1.0 6.6495e-04 1.5 0.00e+00 0.0 0.0e+00 0.0e+00 2.0e+00  0  0  0  0  1   9  0  0  0100     0
    PCSetUp                2 1.0 5.4271e-03 1.2 5.30e+06 1.0 0.0e+00 0.0e+00 0.0e+00  3  2  0  0  0  90100  0  0  0  3907
    PCSetUpOnBlocks        1 1.0 5.3999e-03 1.2 5.30e+06 1.0 0.0e+00 0.0e+00 0.0e+00  3  2  0  0  0  90100  0  0  0  3927

    --- Event Stage 6: KSPSolve 1

    MatMult               60 1.0 2.4068e-02 1.1 6.54e+07 1.0 6.0e+02 2.1e+04 0.0e+00 12 27 73 37  0  32 28100100  0 10731
    MatSolve              61 1.0 1.9177e-02 1.0 5.99e+07 1.0 0.0e+00 0.0e+00 0.0e+00 10 25  0  0  0  26 26  0  0  0 12491
    VecMDot               59 1.0 1.4741e-02 1.3 4.86e+07 1.0 0.0e+00 0.0e+00 5.9e+01  7 21  0  0 27  18 21  0  0 49 13189
    VecNorm               61 1.0 3.0417e-03 1.4 3.29e+06 1.0 0.0e+00 0.0e+00 6.1e+01  1  1  0  0 28   4  1  0  0 51  4332
    VecScale              61 1.0 9.9802e-04 1.0 1.65e+06 1.0 0.0e+00 0.0e+00 0.0e+00  1  1  0  0  0   1  1  0  0  0  6602
    VecCopy                2 1.0 5.9128e-05 1.4 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0
    VecSet                64 1.0 8.0323e-04 1.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   1  0  0  0  0     0
    VecAXPY                3 1.0 7.4387e-05 1.1 1.62e+05 1.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0  8712
    VecMAXPY              61 1.0 8.8558e-03 1.1 5.18e+07 1.0 0.0e+00 0.0e+00 0.0e+00  5 22  0  0  0  12 23  0  0  0 23393
    VecScatterBegin       60 1.0 9.6416e-04 1.8 0.00e+00 0.0 6.0e+02 2.1e+04 0.0e+00  0  0 73 37  0   1  0100100  0     0
    VecScatterEnd         60 1.0 6.1543e-03 1.2 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  3  0  0  0  0   8  0  0  0  0     0
    VecNormalize          61 1.0 4.2675e-03 1.3 4.94e+06 1.0 0.0e+00 0.0e+00 6.1e+01  2  2  0  0 28   5  2  0  0 51  4632
    KSPGMRESOrthog        59 1.0 2.2627e-02 1.1 9.72e+07 1.0 0.0e+00 0.0e+00 5.9e+01 11 41  0  0 27  29 42  0  0 49 17185
    KSPSolve               1 1.0 7.2577e-02 1.0 2.31e+08 1.0 6.0e+02 2.1e+04 1.2e+02 39 98 73 37 56  99100100100100 12673
    PCSetUpOnBlocks        1 1.0 9.5367e-07 0.0 0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0
    PCApply               61 1.0 2.0427e-02 1.0 5.99e+07 1.0 0.0e+00 0.0e+00 0.0e+00 11 25  0  0  0  28 26  0  0  0 11726
    ------------------------------------------------------------------------------------------------------------------------
    .... [Conclusion of overall summary, see part I] ...

As discussed in the preceding section, the performance summaries for higher-level PETSc routines include the statistics for the lower levels of which they are made up. For example, the communication within matrix-vector products `MatMult()` consists of vector scatter operations, as given by the routines `VecScatterBegin()` and `VecScatterEnd()`.

The final data presented are the percentages of the various statistics (time (`%T`), flop/sec (`%F`), messages(`%M`), average message length (`%L`), and reductions (`%R`)) for each event relative to the total computation and to any user-defined stages (discussed in Profiling Multiple Sections of Code). These statistics can aid in optimizing performance, since they indicate the sections of code that could benefit from various kinds of tuning. Hints for Performance Tuning gives suggestions about achieving good performance with PETSc codes.

The additional option `-log_view_memory` causes the display of additional columns of information about how much memory was allocated and freed during each logged event. This is useful to understand what phases of a computation require the most memory.

### Using `-log_mpe` with Jumpshot

It is also possible to use the Jumpshot package [HL91] to visualize PETSc events. This package comes with the MPE software, which is part of the MPICH [Getal] implementation of MPI. The option

    -log_mpe [logfile]

creates a logfile of events appropriate for viewing with Jumpshot. The user can either use the default logging file or specify a name via `logfile`. Events can be deactivated as described in Restricting Event Logging.

The user can also log MPI events. To do this, simply consider the PETSc application as any MPI application, and follow the MPI implementation’s instructions for logging MPI calls. For example, when using MPICH, this merely required adding `-llmpich` to the library list before `-lmpich`.

### Profiling Nested Events

It is possible to output the PETSc logging information in a nested format where the hierarchy of events is explicit. This output can be generated either as an XML file or as a text file in a format suitable for viewing as a flame graph.

One can generate the XML output by passing the option `-log_view :[logfilename]:ascii_xml`. It can be viewed by copying `${PETSC_DIR}/share/petsc/xml/performance_xml2html.xsl` into the current directory, then opening the logfile in your browser.

The flame graph output can be generated with the option `-log_view :[logfile]:ascii_flamegraph`. It can then be visualised with either FlameGraph or speedscope. A flamegraph can be visualized directly from stdout using, for example, `ImageMagick's display utility <https://imagemagick.org/script/display.php>`:

    cd $PETSC_DIR/src/sys/tests
    make ex30
    mpiexec -n 2 ./ex30 -log_view ::ascii_flamegraph | flamegraph | display

Note that user-defined stages (see Profiling Multiple Sections of Code) will be ignored when using this nested format.

## Profiling Application Codes

PETSc automatically logs object creation, times, and floating-point counts for the library routines. Users can easily supplement this information by profiling their application codes as well. The basic steps involved in logging a user-defined portion of code, called an event, are shown in the code fragment below:

    PetscLogEvent  USER_EVENT;
    PetscClassId   classid;
    PetscLogDouble user_event_flops;

    PetscClassIdRegister("class name",&classid);
    PetscLogEventRegister("User event name",classid,&USER_EVENT);
    PetscLogEventBegin(USER_EVENT,0,0,0,0);
    /* code segment to monitor */
    PetscLogFlops(user_event_flops);
    PetscLogEventEnd(USER_EVENT,0,0,0,0);

One must register the event by calling `PetscLogEventRegister()`, which assigns a unique integer to identify the event for profiling purposes:

    PetscLogEventRegister(const char string[],PetscClassId classid,PetscLogEvent *e);

Here `string` is a user-defined event name, and `color` is an optional user-defined event color (for use with Jumpshot logging; see Using -log_mpe with Jumpshot); one should see the manual page for details. The argument returned in `e` should then be passed to the `PetscLogEventBegin()` and `PetscLogEventEnd()` routines.

Events are logged by using the pair

    PetscLogEventBegin(int event,PetscObject o1,PetscObject o2,PetscObject o3,PetscObject o4);
    PetscLogEventEnd(int event,PetscObject o1,PetscObject o2,PetscObject o3,PetscObject o4);

The four objects are the PETSc objects that are most closely associated with the event. For instance, in a matrix-vector product they would be the matrix and the two vectors. These objects can be omitted by specifying 0 for `o1` - `o4`. The code between these two routine calls will be automatically timed and logged as part of the specified event.

Events are collective by default on the communicator of `o1` (if present). They can be made not collective by using `PetscLogEventSetCollective()`. No synchronization is performed on collective events in optimized builds unless the command line option `-log_sync` is used; however, we do check for collective semantics in debug mode.

The user can log the number of floating-point operations for this segment of code by calling

    PetscLogFlops(number of flop for this code segment);

between the calls to `PetscLogEventBegin()` and `PetscLogEventEnd()`. This value will automatically be added to the global flop counter for the entire program.

## Profiling Multiple Sections of Code

By default, the profiling produces a single set of statistics for all code between the `PetscInitialize()` and `PetscFinalize()` calls within a program. One can independently monitor several “stages” of code by switching among the various stages with the commands

    PetscLogStagePush(PetscLogStage stage);
    PetscLogStagePop();

see the manual pages for details. The command

    PetscLogStageRegister(const char *name,PetscLogStage *stage)

allows one to associate a name with a stage; these names are printed whenever summaries are generated with `-log_view`. The following code fragment uses three profiling stages within an program.

    PetscInitialize(int *argc,char ***args,0,0);
    /* stage 0 of code here */
    PetscLogStageRegister("Stage 0 of Code", &stagenum0);
    for (i=0; i<ntimes; i++) {
        PetscLogStageRegister("Stage 1 of Code", &stagenum1);
        PetscLogStagePush(stagenum1);
        /* stage 1 of code here */
        PetscLogStagePop();
        PetscLogStageRegister("Stage 2 of Code", &stagenum2);
        PetscLogStagePush(stagenum2);
        /* stage 2 of code here */
        PetscLogStagePop();
    }
    PetscFinalize();

The listings above show output generated by `-log_view` for a program that employs several profiling stages. In particular, this program is subdivided into six stages: loading a matrix and right-hand-side vector from a binary file, setting up the preconditioner, and solving the linear system; this sequence is then repeated for a second linear system. For simplicity, the second listing contains output only for stages 5 and 6 (linear solve of the second system), which comprise the part of this computation of most interest to us in terms of performance monitoring. This code organization (solving a small linear system followed by a larger system) enables generation of more accurate profiling statistics for the second system by overcoming the often considerable overhead of paging, as discussed in Accurate Profiling and Paging Overheads.

## Restricting Event Logging

By default, all PETSc operations are logged. To enable or disable the PETSc logging of individual events, one uses the commands

    PetscLogEventActivate(PetscLogEvent event);
    PetscLogEventDeactivate(PetscLogEvent event);

The `event` may be either a predefined PETSc event (as listed in the file `$PETSC_DIR/include/petsclog.h`) or one obtained with `PetscLogEventRegister()` (as described in Profiling Application Codes).

PETSc also provides routines that deactivate (or activate) logging for entire components of the library. Currently, the components that support such logging (de)activation are `Mat` (matrices), `Vec` (vectors), `KSP` (linear solvers, including `KSP` and `PC`), and `SNES` (nonlinear solvers):

    PetscLogEventDeactivateClass(MAT_CLASSID);
    PetscLogEventDeactivateClass(KSP_CLASSID); /* includes PC and KSP */
    PetscLogEventDeactivateClass(VEC_CLASSID);
    PetscLogEventDeactivateClass(SNES_CLASSID);

and

    PetscLogEventActivateClass(MAT_CLASSID);
    PetscLogEventActivateClass(KSP_CLASSID);   /* includes PC and KSP */
    PetscLogEventActivateClass(VEC_CLASSID);
    PetscLogEventActivateClass(SNES_CLASSID);

## Interpreting `-info` Output: Informative Messages

Users can activate the printing of verbose information about algorithms, data structures, etc. to the screen by using the option `-info` or by calling `PetscInfoAllow(PETSC_TRUE)`. Such logging, which is used throughout the PETSc libraries, can aid the user in understanding algorithms and tuning program performance. For example, as discussed in Sparse Matrices, `-info` activates the printing of information about memory allocation during matrix assembly.

One can selectively turn off informative messages about any of the basic PETSc objects (e.g., `Mat`, `SNES`) with the command

    PetscInfoDeactivateClass(int object_classid)

where `object_classid` is one of `MAT_CLASSID`, `SNES_CLASSID`, etc. Messages can be reactivated with the command

    PetscInfoActivateClass(int object_classid)

Such deactivation can be useful when one wishes to view information about higher-level PETSc libraries (e.g., `TS` and `SNES`) without seeing all lower level data as well (e.g., `Mat`).

One can turn on or off logging for particular classes at runtime

    -info [filename][:[~]<list,of,classnames>[:[~]self]]

The `list,of,classnames` is a list, separated by commas with no spaces, of classes one wishes to view the information on. For example `vec,ksp`. Information on all other classes will not be displayed. The ~ indicates to not display the list of classes but rather to display all other classes.

`self` indicates to display information on objects that are associated with `PETSC_COMM_SELF` while `~self` indicates to display information only for parallel objects.

See `PetscInfo()` for links to all the info operations that are available.

Application programmers can log their own messages, as well, by using the routine

    PetscInfo(void* obj,char *message,...)

where `obj` is the PETSc object associated most closely with the logging statement, `message`. For example, in the line search Newton methods, we use a statement such as

    PetscInfo(snes,"Cubic step, lambda %g\n",lambda);

## Time

PETSc application programmers can access the wall clock time directly with the command

    PetscLogDouble time;
    PetscCall(PetscTime(&time));

which returns the current time in seconds since the epoch, and is commonly implemented with `MPI_Wtime`. A floating point number is returned in order to express fractions of a second. In addition, as discussed in Profiling Application Codes, PETSc can automatically profile user-defined segments of code.

## Saving Output to a File

All output from PETSc programs (including informative messages, profiling information, and convergence data) can be saved to a file by using the command line option `-history [filename]`. If no file name is specified, the output is stored in the file `${HOME}/.petschistory`. Note that this option only saves output printed with the `PetscPrintf()` and `PetscFPrintf()` commands, not the standard `printf()` and `fprintf()` statements.

## Accurate Profiling and Paging Overheads

One factor that often plays a significant role in profiling a code is paging by the operating system. Generally, when running a program, only a few pages required to start it are loaded into memory rather than the entire executable. When the execution proceeds to code segments that are not in memory, a pagefault occurs, prompting the required pages to be loaded from the disk (a very slow process). This activity distorts the results significantly. (The paging effects are noticeable in the log files generated by `-log_mpe`, which is described in Using -log_mpe with Jumpshot.)

To eliminate the effects of paging when profiling the performance of a program, we have found an effective procedure is to run the exact same code on a small dummy problem before running it on the actual problem of interest. We thus ensure that all code required by a solver is loaded into memory during solution of the small problem. When the code proceeds to the actual (larger) problem of interest, all required pages have already been loaded into main memory, so that the performance numbers are not distorted.

When this procedure is used in conjunction with the user-defined stages of profiling described in Profiling Multiple Sections of Code, we can focus easily on the problem of interest. For example, we used this technique in the program KSP Tutorial ex10 to generate the timings within listing and listing. In this case, the profiled code of interest (solving the linear system for the larger problem) occurs within event stages 5 and 6. Interpreting -log_view Output: Parallel Performance provides details about interpreting such profiling data.

In particular, the macros

    PetscPreLoadBegin(PetscBool flag,char* stagename)
    PetscPreLoadStage(char *stagename)

and

    PetscPreLoadEnd()

can be used to easily convert a regular PETSc program to one that uses preloading. The command line options `-preload` `true` and `-preload` `false` may be used to turn on and off preloading at run time for PETSc programs that use these macros.

## NVIDIA Nsight Systems profiling

Nsight Systems will generate profiling data with a CUDA executable with the command `nsys`. For example, in serial

    nsys profile -t nvtx,cuda -o file --stats=true --force-overwrite true ./a.out

will generate a file `file.qdstrm` with performance data that is annotated with PETSc events (methods) and Kokkos device kernel names. The Nsight Systems GUI, `nsys-ui`, can be used to navigate this file (https://developer.nvidia.com/nsight-systems). The Nsight Systems GUI lets you see a timeline of code performance information like kernels, memory mallocs and frees, CPU-GPU communication, and high-level data like time, sizes of memory copies, and more, in a popup window when the mouse hovers over the section. To view the data, start `nsys-ui` without any arguments and then `Import` the `.qdstrm` file in the GUI. A side effect of this viewing process is the generation of a file `file.nsys-rep`, which can be viewed directly with `nsys-ui` in the future.

For an MPI parallel job, only one process can call `nsys`, say have rank zero output `nsys` data and have all other ranks call the executable directly. For example with MPICH or Open MPI - we can run a parallel job on 4 MPI tasks as:

    mpiexec -n 1 nsys profile -t nvtx,cuda -o file_name --stats=true --force-overwrite true ./a.out : -n 3 ./a.out

Note: The Nsight GUI can open profiling reports from elsewhere. For example, a report from a compute node can be analyzed on your local machine, but care should be taken to use the exact same versions of Nsight Systems that generated the report. To check the version of Nsight on the compute node run `nsys-ui` and note the version number at the top of the window.

## ROCProfiler profiling

For AMD GPUs, log events registered to PETSc can be displayed as ranges in trace files generated by `rocprof` by running with the `-log_roctx` flag. See the `rocprof` documentation for details of how to run the profiler. At the least, you will need:

    mpiexec -n 1 rocprofv3 --marker-trace -o file_name -- ./path/to/application -log_roctx

## Using TAU

TAU profiles can be generated without the need for instrumentation through the use of the perfstubs package. PETSc by default is configured with `--with-tau-perfstubs`. To generate profiles with TAU, first setup TAU:

    wget http://tau.uoregon.edu/tau.tgz
    ./configure -cc=mpicc -c++=mpicxx -mpi -bfd=download -unwind=download && make install
    export PATH=<tau dir>/x86_64/bin:$PATH

For more information on configuring TAU, see http://tau.uoregon.edu. Next, run your program with TAU. For instance, to profile `ex56`,

    cd $PETSC_DIR/src/snes/tutorials
    make ex56
    mpirun -n 4 tau_exec -T mpi ./ex56 -log_perfstubs <args>

This should produce four `profile.*` files with profile data that can be viewed with `paraprof/pprof`:

    Reading Profile files in profile.*

    NODE 0;CONTEXT 0;THREAD 0:
    ---------------------------------------------------------------------------------------
    %Time    Exclusive    Inclusive       #Call      #Subrs  Inclusive Name
                  msec   total msec                          usec/call
    ---------------------------------------------------------------------------------------
    100.0           26        1,838           1       41322    1838424 .TAU application
     73.2            1        1,345           2         168     672950 SNESSolve
     62.2            3        1,142           2        1282     571442 SNESJacobianEval
     62.0        1,136        1,138           2          76     569494 DMPlexJacobianFE
     60.1        0.046        1,105           1          32    1105001 Solve 1
     15.2           87          279           5       11102      55943 Mesh Setup
     13.2        0.315          241           1          32     241765 Solve 0
      7.8           80          144       38785       38785          4 MPI_Allreduce()
      7.0           69          128           6       43386      21491 DualSpaceSetUp
      6.2            1          114           4          54      28536 PCSetUp
      6.0           12          110           2         892      55407 PCSetUp_GAMG+
      3.9           70           70           1           0      70888 MPI_Init_thread()
      3.7           68           68       41747           0          2 MPI Collective Sync
      3.6            8           66           4        3536      16548 SNESFunctionEval
      2.6           45           48         171         171        281 MPI_Bcast()
      1.9           34           34        7836           0          4 MPI_Barrier()
      1.8        0.567           33           2          68      16912  GAMG Coarsen

[Getal]

William Gropp and et. al. MPICH Web page. http://www.mpich.org. URL: http://www.mpich.org.

[HL91]

Virginia Herrarte and Ewing Lusk. Studying parallel program behavior with Upshot. Technical Report ANL-91/15, Argonne National Laboratory, August 1991.