Profiling Fortran code

Install for this page: pip install scope-profiler supplies the Fortran source and requires a Fortran 2008 compiler. For the Python/F2PY example, use pip install "scope-profiler[fortran]" to add Meson and Ninja.

scope-profiler ships a Fortran module with the same region model as the Python API. A Fortran program marks regions, writes a small trace file per rank, and scope-profiler import-native turns those traces into the usual HDF5 output — so a Fortran run gets the same summaries, charts, exporters and plot workflow as a Python one.

The module is deliberately undemanding: plain Fortran 2008 with iso_c_binding, one file, no preprocessor flags, no HDF5, no MPI, nothing to link beyond libc.

The trace format is shared with the C API, so a program built from both lands in a single profile.

Build and run a first profile

The commands below use GNU Fortran on Linux or macOS. Install the Python package in an activated environment; the compiled Fortran executable itself needs no Python interpreter. Python is used to locate the module and to convert and inspect the recorded trace afterwards.

python -m pip install scope-profiler
sp_source="$(python -c 'from scope_profiler.native_trace import fortran_source_path; print(fortran_source_path())')"
mkdir -p build traces

When working from a checkout, use python -m pip install -e . instead to ensure the compiler picks up the local API. You can also copy src/scope_profiler/fortran/scope_profiler.f90 into your own project.

Save this complete program as simulation.f90. It excludes five warm-up iterations, measures 100 production iterations, and saves a snapshot every 25 iterations. Replace solve_system with your application’s work.

program simulation
   use scope_profiler
   use, intrinsic :: iso_fortran_env, only: real64, error_unit
   implicit none
   integer :: step, solve, status
   character(len=512) :: message
   type(sp_region_stats) :: stats
   real(real64) :: checksum, total_seconds, mean_seconds

   call sp_init("traces/profile", stat=status, errmsg=message)
   call check_status()
   solve = sp_region("solve", file="simulation.f90", stat=status, errmsg=message)
   call check_status()
   checksum = 0.0_real64

   do step = 1, 5
      call sp_begin(solve)
      call solve_system(checksum)
      call sp_end(solve)
   end do
   call sp_reset(stat=status, errmsg=message) ! same handle, fresh measurements
   call check_status()

   do step = 1, 100
      call sp_begin(solve)
      call solve_system(checksum)
      call sp_end(solve)
      if (mod(step, 25) == 0) then
         call sp_flush(stat=status, errmsg=message)
         call check_status()
      end if
   end do

   call sp_get_region_stats(solve, stats, stat=status, errmsg=message)
   call check_status()
   total_seconds = real(stats%total_ns, real64)*1.0e-9_real64
   mean_seconds = 0.0_real64
   if (stats%calls > 0) mean_seconds = total_seconds/real(stats%calls, real64)
   print *, "completed calls:", stats%calls
   print *, "total / mean seconds:", total_seconds, mean_seconds
   print *, "checksum:", checksum

   call sp_finalize(stat=status, errmsg=message)
   call check_status()

contains

   subroutine solve_system(value)
      real(real64), intent(inout) :: value
      integer :: i
      do i = 1, 10000
         value = value + sqrt(real(i, real64))
      end do
   end subroutine solve_system

   subroutine check_status()
      if (status == SP_OK) return
      write (error_unit, '(a)') trim(message)
      error stop 1
   end subroutine check_status
end program simulation

Compile the profiler module first, then compile and link the application:

gfortran -std=f2008 -O2 -Jbuild -c "$sp_source" -o build/scope_profiler.o
gfortran -std=f2008 -O2 -Ibuild simulation.f90 build/scope_profiler.o -o build/simulation
./build/simulation
python -m scope_profiler import-native traces -o build/profiling_data.h5
python -m scope_profiler inspect build/profiling_data.h5
python -m scope_profiler plot default build/profiling_data.h5 -o build/figures

Run these commands from the directory containing simulation.f90. The program writes traces/profile_rank00000.spt; its solve region should have exactly 100 calls. The checksum includes warm-up work, but the profiling measurements do not. Keep traces from different runs in different directories so the importer does not find multiple files claiming the same rank.

Integrate the module into an existing build

Compile scope_profiler.f90 before every source that uses its module. With GNU Fortran, -Jbuild selects where to write scope_profiler.mod and -Ibuild lets dependent sources find it.
Link scope_profiler.o into the final executable as well as making the .mod file available during compilation. The .mod file alone does not contain the implementation.
Use the same Fortran compiler and compatible options for the profiler and its callers. Rebuild the .mod file when switching compilers. Other compilers may use different module-directory flags.
Create the output directory before running. sp_init stores the output prefix; it does not create directories or test whether the file is writable. Those failures are reported by sp_flush or sp_finalize.

The repository also contains ready-made examples:

# From the repository root, with the package installed in this environment:
make -C examples/fortran run-standalone FC=gfortran PYTHON=python

That target compiles the example, runs it, and converts the trace into examples/fortran/build/profiling_data.h5. A smaller Makefile and example.f90 are also shipped beside the installed profiler module.

Marking regions

For an existing simulation, place calls around the operations you want to measure. In this outline, assemble_matrix and solve_system are your application routines:

program simulation
   use scope_profiler
   implicit none
   integer :: step, solve, assemble

   call sp_init("profile")            ! output prefix

   solve = sp_region("solve")         ! resolve names once...
   assemble = sp_region("assemble")

   do step = 1, 1000
      call sp_begin(assemble)         ! ...then use the handles
      call assemble_matrix()
      call sp_end(assemble)

      call sp_begin(solve)
      call solve_system()
      call sp_end(solve)
   end do

   call sp_finalize()                 ! writes profile_rank00000.spt
end program simulation

sp_begin_name("solve") / sp_end_name("solve") take the name directly, which reads better in cold code; they look the name up on every call, so prefer the handle form in hot loops.

Regions may nest, and a region may re-enter itself recursively — each entry reserves its own slot, exactly as in the Python API.

Nesting must be proper: a region entered inside another has to be left before it. Post-processing reconstructs the call graph from the recorded intervals, so a sp_begin/sp_end pair that crosses another region’s boundary — rather than sitting inside or outside it — has no call stack to recover, and reading the trace back raises NestingError. The timings themselves are still recorded and readable; it is the nesting, the flame chart and the exclusive times that cannot be derived.

The API

Session lifecycle

Use one initialization per measurement session and resolve handles afterwards. There is one global profiler per process; independently instrumented libraries share its region registry and output prefix.

| Operation | What happens to handles and measurements | | — | — | | sp_init | A new session replaces the previous recording. Register handles again. Save the old session first if needed. | | sp_flush | Handles, measurements, and open calls remain valid. Only completed calls appear in the snapshot. | | sp_reset | Handles and source metadata survive; call counts and recorded timings are cleared. All calls must be closed. | | sp_finalize | Recording stops after successful output. Entry counts and completed-call statistics remain readable. |

Before initialization or after finalization, begin/end calls do not record. Do not cache a handle returned before initialization: sp_region returns 0 when inactive. Likewise, a handle from a previous session must be resolved again after another sp_init. Use sp_is_active() to check session state.

Error handling

sp_init, sp_region, sp_begin, sp_end, their name-based variants, sp_flush, sp_reset, sp_get_region_stats, and sp_finalize accept optional stat= (default integer) and errmsg= (character) outputs. Success sets stat to SP_OK and clears errmsg. Supplying either output suppresses stderr diagnostics; without them, errors are reported on stderr. Instrumentation calls on an inactive profiler remain quiet no-ops, with SP_ERR_INACTIVE returned when requested. Repeated finalization succeeds.

integer :: status
character(len=512) :: message

call sp_init("profile", stat=status, errmsg=message)
if (status /= SP_OK) then
   print *, trim(message)
end if

Choose a sufficiently long message buffer, such as character(len=512); Fortran truncates the diagnostic to fit a shorter buffer. Read the outputs after each call, because the next call overwrites them.

| Status | Meaning and next step | | — | — | | SP_OK | The operation succeeded; errmsg is empty. | | SP_ERR_INACTIVE | Initialize before recording, flushing, or resetting. Statistics can still be read after finalization. | | SP_ERR_NO_CLOCK | A supported monotonic clock could not be read. Check platform support; a failed timing call is not a valid measurement. | | SP_ERR_NO_MEMORY | Allocation failed. Existing recordings survive failed growth/flush; reduce recording volume or free memory before retrying. A failed initialization may have replaced the previous session. | | SP_ERR_IO | Check the output directory, permissions, quota, and free space. A failed snapshot may be incomplete; correct the cause and retry output. | | SP_ERR_UNMATCHED_END | Check for an extra end call or a return path that did not enter the region. | | SP_ERR_OPEN_SCOPES | Reset was refused, or finalization saved completed calls while dropping unfinished ones. Close regions before these operations. | | SP_ERR_INVALID_ARGUMENT | Check the region handle, prefix length, and rank. | | SP_ERR_DEPTH | The per-region recursion limit was exceeded, or an enclosing entry failed. Its matching end must still be called. |

A failed sp_region returns handle 0. Prefixes must contain 1–512 non-trailing-blank characters and ranks must be nonnegative.

sp_finalize reports SP_ERR_OPEN_SCOPES when it saves completed calls but drops unfinished calls, including retaining completed recursive children. On allocation or output failure, flush/finalize retain the measurements and leave the session active so the application can correct the cause and retry. Output errors take precedence over unfinished-call diagnostics.

The quick-start program treats profiling failures as fatal for clarity. In a long-running simulation you may instead log the failure and continue the calculation. On output failure, retry sp_flush or sp_finalize after fixing the cause; calling sp_init would replace the measurements you want to save.

Keep begin/end calls paired even when an entry fails for lack of memory, a clock error, or the recursion limit. A valid region’s rejected entry tracks its matching end so that it cannot accidentally close an outer invocation. If a region was never registered successfully (handle 0), skip its instrumentation or register it again before beginning work.

Snapshots, measurement windows, and statistics

sp_flush() writes all completed calls so far to the usual trace path and continues recording. Open calls are omitted until they finish. Each flush replaces the previous snapshot; it does not append, clear buffers, or provide an atomic checkpoint. A failed write may leave an incomplete file. Flush uses temporary storage of 16 bytes per completed call in the largest region, in addition to the recording buffers.

sp_reset() clears measurements and entry counts while preserving handles, source locations, and allocated recording capacity. It refuses with SP_ERR_OPEN_SCOPES if any region is open, leaving all measurements intact. Use it after warm-up or between measurement windows. Reset does not remove an existing snapshot; the next flush/finalize replaces it.

The complete program at the start of this guide demonstrates this sequence: reset after warm-up, query statistics, flush periodically, and finalize.

sp_region_stats has four integer(int64) fields: calls, total_ns, min_ns, and max_ns. These describe completed calls only, use inclusive durations in nanoseconds, and are all zero when no calls have completed. Statistics are computed on request by scanning the recorded calls, adding no running statistics updates to sp_begin/sp_end. They remain readable after finalization. In contrast, sp_num_calls counts successful entries, including calls that are still open. Recording buffers are retained after finalization and released when initialization replaces the session’s storage.

For seconds, convert with real(stats%total_ns, real64)*1.0e-9_real64 after importing real64 from iso_fortran_env. Compute the mean only when stats%calls > 0, as in the complete example above. Inclusive time contains work in nested regions, so adding an outer region’s total to its children’s totals double-counts that work. Use imported results for call-tree and exclusive-time analysis.

Choose snapshot and reset boundaries

For a long run, flush at completed timestep or checkpoint boundaries. Calling flush while an outer region is open is allowed, but the snapshot omits that outer call and may show completed children as roots until it closes. Wait until a flush returns successfully before importing its file, and avoid reading the path while another flush is replacing it.

Flush retains all measurements, so repeated snapshots are cumulative and do not bound memory use. Recording uses two growing timestamp arrays per region (16 bytes per allocated call slot); growth can reserve unused capacity. Reset lets later calls reuse that capacity but does not shrink it.

For separate measurement windows:

Close all regions and call sp_flush, checking its status.
Copy or convert the snapshot to a separate archive location if you need to retain that window. Do this before the next flush replaces it.
Call sp_reset, checking that it succeeded, then continue using the same handles.

Keep archived windows outside the directory passed to import-native: files from separate windows can claim the same rank, and the importer rejects that ambiguity. For statistics-only progress reports, call sp_get_region_stats at reporting boundaries rather than every iteration; each query scans the recorded calls.

Source locations

sp_region(name, file="solver.f90", line=42) stores optional source metadata in the trace and imported HDF5 output. An existing region registered without a source file can acquire one later. The first nonempty source file wins; later registrations do not replace its file or line. A missing or negative line means unknown; a line without a file is ignored. No preprocessing is required, and cached-handle begin/end calls do not process source metadata.

The file and line are supplied by you; registration does not inspect the calling source automatically. For example, place this beside the code whose location you want the profile to identify, and update the line when that code moves:

solve = sp_region("solve", file="src/solver.f90", line=42)

Source locations do not distinguish regions with the same name. Use distinct names such as fluid:solve and particles:solve when they should have separate measurements. A relative source path is usually sufficient for identifying code without embedding a machine-specific absolute path.

Under MPI

Pass each rank’s id so the traces do not collide:

call MPI_Comm_rank(MPI_COMM_WORLD, my_rank, ierr)
call sp_init("profile", rank=my_rank)

Every rank writes its own file; nothing is communicated, so sp_finalize() is not collective and a rank that dies takes only its own trace with it. The importer merges whatever it finds. Create the shared output directory before launching, and initialize the profiler after MPI_Init/MPI_Comm_rank. Finalize the profiler on each rank before MPI_Finalize. The profiler itself makes no MPI calls, and flushing one rank does not wait for others.

Use your MPI compiler wrapper to build the application and the profiler with the same underlying compiler. After compiling your MPI-aware simulation, run it from a fresh trace directory:

mkdir -p mpi-run
(cd mpi-run && mpirun -n 128 ../simulation)
python -m scope_profiler import-native mpi-run -o mpi-run/profiling_data.h5
python -m scope_profiler plot default mpi-run/profiling_data.h5 -o mpi-run/figures

From Python

from scope_profiler.native_trace import load_traces, convert_traces

results = load_traces("run_dir")  # -> ProfilingResults, as usual
results.print_summary()
results.to_dataframe()

convert_traces("run_dir", "profiling_data.h5", label="128 ranks")

load_traces returns the very same ProfilingResults a Python run produces, so every method, plot_* function and exporter works on it unchanged.

One timeline with Python

The module reads the same OS clock CPython’s time.perf_counter_ns() uses — CLOCK_MONOTONIC on Linux, CLOCK_UPTIME_RAW on macOS — and picks it by probing at run time rather than by compile-time platform macros, which gfortran does not reliably define.

That means Fortran and Python regions recorded in the same process tree share an epoch: a Python driver’s timestamps and its Fortran kernels’ timestamps are directly comparable, and can be read on one timeline.

Python calling Fortran

The case this is really for: a Python driver over Fortran kernels, where both sides mark regions and you want one profile. Because the two APIs read the same clock, their timestamps are directly comparable — the Fortran regions nest inside the Python region that called them, and call_stack() sees a single tree spanning both languages.

Compile scope_profiler.f90 alongside the kernels using f2py, ctypes, Cython, or your existing extension build. With f2py, restrict Python wrappers using only: to your kernel entry points. The profiler’s derived types and named integer kinds are intended for Fortran callers, not direct f2py wrapping. Expose wrappers for initialization and finalization alongside the computational routines.

For example, this complete kernel module can be saved as kernels.f90:

module kernels
   use scope_profiler
   implicit none
   integer :: solve_id = 0
contains
   subroutine start_profiling(prefix, rank)
      character(len=*), intent(in) :: prefix
      integer, intent(in) :: rank
      call sp_init(prefix, rank=rank)
      solve_id = sp_region("fortran:solve", file="kernels.f90")
   end subroutine start_profiling

   subroutine stop_profiling()
      call sp_finalize()
   end subroutine stop_profiling

   subroutine solve(n, value)
      integer, intent(in) :: n
      double precision, intent(out) :: value
      integer :: i
      call sp_begin(solve_id)
      value = 0.0d0
      do i = 1, n
         value = value + sqrt(dble(i))
      end do
      call sp_end(solve_id)
   end subroutine solve
end module kernels

Build it from the directory containing kernels.f90:

python -m pip install "scope-profiler[fortran]"
sp_source="$(python -c 'from scope_profiler.native_trace import fortran_source_path; print(fortran_source_path())')"
python -m numpy.f2py -c "$sp_source" kernels.f90 -m kernels --backend meson \
  only: start_profiling stop_profiling solve :

Keep meson and ninja on PATH by activating the environment where the Fortran extra is installed. Then save this as run_mixed.py beside the built extension and run it with python run_mixed.py. finalize() folds the Fortran trace into the Python result:

from pathlib import Path

import kernels
from scope_profiler import ProfileManager

Path("mixed-traces").mkdir(exist_ok=True)
ProfileManager.setup(file_path="mixed_profile.h5")
kernels.kernels.start_profiling("mixed-traces/trace", 0)  # serial rank

for step in range(3):
    with ProfileManager.region("python:step"):
        value = kernels.kernels.solve(10000)

kernels.kernels.stop_profiling()
ProfileManager.finalize(native_traces="mixed-traces")

The resulting mixed_profile.h5 contains three python:step calls and three fortran:solve calls nested inside them. Inspect it with python -m scope_profiler inspect mixed_profile.h5. The repository’s examples/fortran directory has a larger example; run it with make -C examples/fortran run-mixed PYTHON=python from the repository root.

Two rules:

Call the Fortran sp_finalize() first. Its trace has to exist by the time finalize() reads it.
Give the two sides distinct region names. A name recorded by both raises, rather than silently double-counting a wrapper and the region inside it. A python: / fortran: prefix is the simplest convention.

Under MPI each rank folds in the trace matching its own rank, so pass the rank to sp_init and nothing else changes — the usual single output file comes out the other end.

Combining afterwards

If the two halves were profiled separately, merge them after the fact:

scope-profiler import-native traces/ --merge python_only.h5 -o combined.h5

or in Python:

from scope_profiler import merge_results, read_h5
from scope_profiler.native_trace import load_traces

combined = merge_results(read_h5("python_only.h5"), load_traces("traces/"))

Troubleshooting

| Symptom | What to check | | — | — | | Compiler cannot find scope_profiler.mod | Compile the profiler first; point the caller’s module search path at its output directory. | | Linker reports undefined profiler symbols | Link the profiler object, built with the same compiler as the caller. | | No trace exists after initialization | Output is written by flush/finalize, not init. Check their status and the process’s working directory. | | Trace file exists but is truncated | Output may have failed. Check status, fix the cause, and retry from the still-active session. | | Snapshot has fewer calls than sp_num_calls | Entry counts include open calls; snapshots and statistics include completed calls only. | | Reset reports open regions | Match every begin with an end, including on early returns, before resetting. | | Imported call graph raises NestingError | Check properly nested begin/end pairs and avoid simultaneous instrumentation from multiple OpenMP threads. | | Import reports duplicate ranks | Use one trace per rank per run directory. Keep archived snapshots and previous runs elsewhere. | | f2py build cannot find Meson/Ninja | Activate the environment containing scope-profiler[fortran] so its tools are on PATH. | | f2py tries to wrap profiler types or kinds | Restrict wrappers with only: to the kernel routines called by Python. |

The trace format

sp_finalize() writes <prefix>_rank<NNNNN>.spt, a small binary file:

char[8]   "SCOPEPRF"
int32     format version (2)
int32     rank
int64     number of regions
per region:
    int32     length of the name in bytes
    char[]    name
    int32     length of source file in bytes (0 if unknown)
    char[]    source file
    int32     source line (-1 if unknown)
    int64     number of calls
    int64[]   start timestamps, nanoseconds
    int64[]   end timestamps, nanoseconds

Native endianness; the reader detects and handles both. 16 bytes per recorded call, the same as the Python side.

The reader also accepts legacy version-1 traces without source locations.

Limitations

No line-level profiling. Regions are timed as a whole; see Line-by-line profiling for how to get line-level detail for native code.
Not thread safe. A region must be entered and left by the same thread. OpenMP threads inside a region are fine — wrap the whole parallel construct in one sp_begin/sp_end from the master thread.
No LIKWID counters. Hardware counters are collected only by the Python API; a Fortran trace carries timings alone.
Names are truncated at SP_MAX_NAME (128) characters, including for lookup. Names sharing those first 128 characters refer to the same region.
Recursion depth per region is capped at SP_MAX_DEPTH (64); deeper entries return SP_ERR_DEPTH and are not timed. Their matching ends are consumed without closing an outer call.
A region still open at sp_finalize() is omitted from the trace and reported with SP_ERR_OPEN_SCOPES. Completed recursive children are kept.