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Merge pull request #42 from score-p/feature/scorep_feedback
Feature/scorep feedback
2 parents 8bcc5fd + b19a0c3 commit d350f21

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.github/workflows/unit_test.yml

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jobs:
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build:
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runs-on: ubuntu-latest
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runs-on: ubuntu-22.04
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steps:
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# Checks-out repository under $GITHUB_WORKSPACE
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- uses: actions/checkout@v4
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- name: Run userpersistence tests
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run: |
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python -m unittest tests/test_userpersistence.py
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python -m unittest tests.test_userpersistence
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- name: Run kernel tests
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run: |
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python -m unittest tests/test_kernel.py
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python -m unittest tests.test_kernel

README.md

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```
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When using persistence in `disk` mode, user can also define directory to which serializer output will be saved with `SCOREP_KERNEL_PERSISTENCE_DIR` environment variable.
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```
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%env SCOREP_KERNEL_PERSISTENCE_DIR=path/to/dir
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```
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To see the detailed report for marshalling steps - `JUMPER_MARSHALLING_DETAILED_REPORT` environment variable can be set.
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```
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%env JUMPER_MARSHALLING_DETAILED_REPORT=1
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```
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You can disable visual animations shown during long-running tasks by setting the `JUMPER_DISABLE_PROCESSING_ANIMATIONS` environment variable.
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```
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%env JUMPER_DISABLE_PROCESSING_ANIMATIONS=1
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```
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`%%execute_with_scorep`
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When dealing with big data structures, there might be a big runtime overhead at the beginning and the end of a Score-P cell. This is due to additional data saving and loading processes for persistency in the background. However this does not affect the actual user code and the Score-P measurements.
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## Logging Configuration
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To adjust logging and obtain more detailed output about the behavior of the JUmPER kernel, refer to the `src/logging_config.py` file.
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This file contains configuration options for controlling the verbosity, format, and destination of log messages. You can customize it to suit your debugging or monitoring needs.
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# Future Work
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The kernel is still under development. The following is on the agenda:

examples/ExampleBasic.ipynb

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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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"source": [
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"### Large array processing with Score-P\n",
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"This example illustrates the steps involved in the marshalling process when a cell instrumented with Score-P is executed with a large data payload as input.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import numpy as np\n",
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"\n",
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"def generate_array_with_size(size_mb, dtype=np.float32):\n",
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" size_bytes = size_mb * 1024 * 1024\n",
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" element_size = np.dtype(dtype).itemsize\n",
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" num_elements = size_bytes // element_size\n",
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" array = np.zeros(num_elements, dtype=dtype)\n",
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" return array\n",
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"\n",
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"big_array = generate_array_with_size(size_mb=1000)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Enable marshalling detailed report for each step."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%env JUMPER_MARSHALLING_DETAILED_REPORT=1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run cell with Score-P"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%execute_with_scorep\n",
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"big_array\n",
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"time.sleep(4)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "scorep-python",
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"display_name": "JUmPER",
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"language": "python",
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"name": "scorep-python"
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"name": "jumper"
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},
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"language_info": {
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"file_extension": ".py",
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"mimetype": "text/plain",
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"name": "Any text"
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"name": "python"
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}
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},
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"nbformat": 4,

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