Hierarchical timing utility with nested context manager support
Project description
muTimer
muTimer is a hierarchical timing utility with nested context manager support. It provides fine-grained timing of code sections.
Features
- Nested timing contexts that track parent-child relationships
- Accumulation of time across multiple calls to the same timer
- Call counting for repeated operations
- Hierarchical summary output in tabular format
- Optional depth limiting for nested timers
- MPI-aware summaries (single output on rank 0 with min/mean/max across ranks)
Usage
from muTimer import Timer
timer = Timer()
with timer("outer"):
# some code
with timer("inner"):
# nested code
with timer("inner"): # called again - time accumulates
# more nested code
timer.print_summary()
Output:
==============================================================================
Timing Summary
==============================================================================
Name Total Calls Average % Parent
------------------------------ ------------ -------- ------------ ----------
outer 22.55 ms 1 - -
inner 12.50 ms 2 6.25 ms 55.4%
(other) 10.06 ms - - 44.6%
==============================================================================
MPI Support
When running under MPI, pass a communicator to avoid every rank printing its own summary. Both mpi4py communicators and muGrid communicators are accepted.
from mpi4py import MPI
from muTimer import Timer
timer = Timer(comm=MPI.COMM_WORLD)
with timer("outer"):
# some code
pass
timer.print_summary() # printed once, on rank 0
Timing data is gathered to rank 0, which prints a single summary showing the mean, minimum and maximum time spent in each section across all ranks:
===========================================================================================
Timing Summary (4 MPI processes)
===========================================================================================
Name Mean Min Max Calls % Parent
------------------------------ ------------ ------------ ------------ -------- ----------
outer 23.30 ms 22.55 ms 24.05 ms 1 -
inner 12.95 ms 12.50 ms 13.40 ms 2 55.6%
(other) 10.35 ms - - - 44.4%
===========================================================================================
If the communicator cannot gather Python objects (no underlying mpi4py communicator), the summary is still printed only on rank 0, using that rank's local timings.
muTimer has no dependency on mpi4py (or any other MPI package): the
communicator is duck-typed. Anything that provides the mpi4py communicator
interface works, e.g. NuMPI's
MPI module, which falls back to a serial stub when mpi4py is not installed:
from NuMPI import MPI # mpi4py if installed, serial stub otherwise
from muTimer import Timer
timer = Timer(comm=MPI.COMM_WORLD)
Memory Tracking
You can also track memory usage (Resident Set Size) by enabling track_memory=True. This requires the psutil package.
import time
from muTimer import Timer
# Create a timer with memory tracking enabled
timer = Timer(track_memory=True)
with timer("outer"):
# allocate some memory
large_list = [0] * 1000000
time.sleep(0.01)
with timer("inner"):
another_list = [1] * 2000000
time.sleep(0.01)
with timer("inner"):
more_memory = [2] * 500000
time.sleep(0.005)
timer.print_summary()
Output:
============================================================================================
Timing Summary
============================================================================================
Name Total Calls Average % Parent Memory
------------------------------ ------------ -------- ------------ ---------- ------------
outer 33.74 ms 1 - - 26.78 MB
inner 20.52 ms 2 10.26 ms 60.8% 19.12 MB
(other) 13.23 ms - - 39.2% 7.66 MB
============================================================================================
License
muTimer is distributed under the MIT License.
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