你已经派生过 gprMax
镜像自地址
https://gitee.com/sunhf/gprMax.git
已同步 2025-08-06 20:46:52 +08:00
Updated instructions to reflect improved benchmarking scripts/procedure.
这个提交包含在:
@@ -23,15 +23,12 @@ The following simple models (found in the ``tests/benchmarking`` sub-package) ca
|
||||
:language: none
|
||||
:linenos:
|
||||
|
||||
The ``#num_threads`` command can be adjusted to benchmark running the code with different numbers of OpenMP threads.
|
||||
|
||||
Using the following steps to collect and report benchmarking results:
|
||||
Using the following steps to collect and report benchmarking results for each of the models:
|
||||
|
||||
1. Run each model with different ``#num_threads`` values - from 1 thread up to the number of physical CPU cores on your machine.
|
||||
2. Note the ``Solving took ..`` time reported by the simulation for each model run.
|
||||
3. Use the ``save_results.py`` script to enter and save your results in a Numpy archive. You will need to enter some machine identification information in the script.
|
||||
4. Use the ``plot_time_speedup.py`` script to create plots of the execution time and speed-up.
|
||||
5. Commit the Numpy archive and plot file using Git
|
||||
1. Run gprMax in benchmarking mode, e.g. ``python -m gprMax tests/benchmarking/bench_100x100x100.in -benchmark``
|
||||
2. Use the ``plot_benchmark`` module to create plots of the execution time and speed-up, e.g. ``python -m tools.benchmarking.plot_benchmark tests/benchmarking/bench_100x100x100.npz``. You will need to enter some machine identification information in the module.
|
||||
3. Commit the Numpy archive and plot file to the GitHub repository
|
||||
|
||||
Results
|
||||
=======
|
||||
|
在新工单中引用
屏蔽一个用户