SWI-Prolog 9.2.9 Reference Manual: Section 4.42

## 4.42 Execution profiling

This section describes the hierarchical execution profiler. This profiler is based on ideas from gprof described in Graham et al., 1982. The profiler consists of two parts: the information-gathering component built into the kernel,164There are two implementations; one based on setitimer() using the `SIGPROF` signal and one using Windows Multi Media (MM) timers. On other systems the profiler is not provided. and a presentation component which is defined in the `library(statistics)` library. The latter can be hooked, which is used by the XPCE module `library(swi/pce_profile)` to provide an interactive graphical frontend for the results.

### 4.42.1 library(prolog_profile): Execution profiler

This module provides a simple frontend on the execution profiler with a hook to the GUI visualiser for profiling results defined in `library(swi/pce_profile)`.

profile(:Goal)
profile(:Goal, +Options)
  Run `once(Goal)` under the execution profiler. If the (xpce) GUI is enabled this predicate is hooked by `library(swi/pce_profile)` and results are presented in a gui that enables navigating the call tree and jump to predicate implementations. Without the GUI, a simple textual report is generated. Defined options are:
  time(Which)
    Profile `cpu` or `wall` time. The default is CPU time.
  sample_rate(Rate)
    Samples per second, any numeric value between 1 and 1000
  ports(Bool)
    Specifies ports counted - `true` (all ports), `false` (call port only) or `classic` (all with some errors). Accomodates space/accuracy tradeoff building call tree.
  top(N)
    When generating a textual report, show the top N predicates.
  cumulative(Bool)
    If `true` (default `false`), show cumulative output in a textual report.
  See also
    show_coverage/2 from `library(test_cover)`.
  To be done
    The textual input reflects only part of the information.
show_profile(+Options)
  Display last collected profiling data. Options are
  top(N)
    When generating a textual report, show the top N predicates.
  cumulative(Bool)
    If `true` (default `false`), show cumulative output in a textual report.
[det]profile_data(-Data)
  Gather all relevant data from profiler. This predicate may be called while profiling is active in which case it is suspended while collecting the data. Data is a dict providing the following fields:
  `summary``:`Dict
    Overall statistics providing

    - samples:Count: Times the statistical profiler was called
    - ticks:Count Virtual ticks during profiling
    - accounting:Count Tick spent on accounting
    - time:Seconds Total time sampled
    - nodes:Count Nodes in the call graph.
    - sample_period: MicroSeconds Same interval timer period in micro seconds
    - ports: Ports One of `true`, `false` or `classic`
  nodes
    List of nodes. Each node provides:

    - predicate:PredicateIndicator
    - ticks_self:Count
    - ticks_siblings:Count
    - call:Count
    - redo:Count
    - exit:Count
    - callers:`list_of(Relative)`
    - callees:`list_of(Relative)`

  Relative is a term of the shape below that represents a caller or callee. Future versions are likely to use a dict instead.

      node(PredicateIndicator, CycleID, Ticks, TicksSiblings,
           Calls, Redos, Exits)

[nondet]profile_procedure_data(?Pred, -Data:dict)
  Collect data for Pred. If Pred is unbound data for each predicate that has profile data available is returned. Data is described in profile_data/1 as an element of the `nodes` key.

### 4.42.2 Visualizing profiling data

Browsing the annotated call-tree as described in section 4.42.3 itself is not very attractive. Therefore, the results are combined per predicate, collecting all callers and callees as well as the propagation of time and activations in both directions. Figure 4 illustrates this. The central yellowish line is the‘current’predicate with counts for time spent in the predicate (`Self’), time spent in its children (`Siblings’), activations through the call and redo ports. Above that are the callers. Here, the two time fields indicate how much time is spent serving each of the callers. The columns sum to the time in the yellowish line. The caller <recursive> is the number of recursive calls. Below the yellowish lines are the callees, with the time spent in the callee itself for serving the current predicate and the time spent in the callees of the callee ('Siblings’), so the whole time-block adds up to the‘Siblings’field of the current predicate. The‘Access’fields show how many times the current predicate accesses each of the callees.

The predicates have a menu that allows changing the view of the detail window to the given caller or callee, showing the documentation (if it is a built-in) and/or jumping to the source.

Figure 4 : Execution profiler showing the activity of the predicate chat:inv_map_list/5.

The statistics shown in the report field of figure 4 show the following information:

- samples
  Number of times the call-tree was sampled for collecting time statistics. On most hardware, the resolution of `SIGPROF` is 1/100 second. This number must be sufficiently large to get reliable timing figures. The Time menu allows viewing time as samples, relative time or absolute time.

- sec
  Total user CPU time with the profiler active.

- predicates
  Total count of predicates that have been called at least one time during the profile.

- nodes
  Number of nodes in the call-tree.

- distortion
  How much of the time is spent building the call-tree as a percentage of the total execution time. Timing samples while the profiler is building the call-tree are not added to the call-tree.

### 4.42.3 Information gathering

While the program executes under the profiler, the system builds a dynamic call-tree. It does this using three hooks from the kernel: one that starts a new goal (profCall), one that tells the system which goal is resumed after an exit (profExit) and one that tells the system which goal is resumed after a fail (i.e., which goal is used to retry (profRedo)). The profCall() function finds or creates the subnode for the argument predicate below the current node, increments the call-count of this link and returns the sub-node which is recorded in the Prolog stack-frame. Choice-points are marked with the current profiling node. profExit() and profRedo() pass the profiling node where execution resumes.

Just using the above algorithm would create a much too big tree due to recursion. For this reason the system performs detection of recursion. In the simplest case, recursive procedures increment the‘recursive’count on the current node. Mutual recursion, however, is not easily detected. For example, call/1 can call a predicate that uses call/1 itself. This can be viewed as a recursive invocation, but this is generally not desirable. Recursion is currently assumed if the same predicate with the same parent appears higher in the call-graph. Early experience with some non-trivial programs are promising.

The last part of the profiler collects statistics on the CPU time used in each node. On systems providing setitimer() with `SIGPROF`, it‘ticks’the current node of the call-tree each time the timer fires. On Windows, a MM-timer in a separate thread checks 100 times per second how much time is spent in the profiled thread and adds this to the current node. See section 4.42.3.1 for details.

#### 4.42.3.1 Profiling in the Windows Implementation

Profiling in the Windows version is similar, but as profiling is a statistical process it is good to be aware of the implementation165We hereby acknowledge Lionel Fourquaux, who suggested the design described here after a newsnet enquiry. for proper interpretation of the results.

Windows does not provide timers that fire asynchronously, frequent and proportional to the CPU time used by the process. Windows does provide multi-media timers that can run at high frequency. Such timers, however, run in a separate thread of execution and they are fired on the wall clock rather than the amount of CPU time used. The profiler installs such a timer running, for saving CPU time, rather inaccurately at about 100 Hz. Each time it is fired, it determines the CPU time in milliseconds used by Prolog since the last time it was fired. If this value is non-zero, active predicates are incremented with this value.