Feature/scorep feedback

.github/workflows/unit_test.yml

@@ -11,7 +11,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     steps:
       # Checks-out repository under $GITHUB_WORKSPACE
       - uses: actions/checkout@v4
@@ -49,8 +49,8 @@ jobs:
 
       - name: Run userpersistence tests
         run: |
-          python -m unittest tests/test_userpersistence.py
+          python -m unittest tests.test_userpersistence
 
       - name: Run kernel tests
         run: |
-          python -m unittest tests/test_kernel.py
+          python -m unittest tests.test_kernel

README.md

@@ -152,6 +152,7 @@ MODE=[disk,memory]
 ```
 
 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.
+To see the detailed report for marshalling steps - `MARSHALLING_DETAILED_REPORT=1` environment variable can be set.
 
 `%%execute_with_scorep`
 
@@ -235,6 +236,11 @@ Similar yields for cloudpickle. Use the `%%marshalling_settings` magic command t
 
 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.
 
+## Logging Configuration
+To adjust logging and obtain more detailed output about the behavior of the JUmPER kernel, refer to the `src/logging_config.py` file.
+
+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.
+
 # Future Work
 
 The kernel is still under development. The following is on the agenda:

examples/ExampleBasic.ipynb

@@ -466,7 +466,57 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "### Large array processing with Score-P\n",
+    "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"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "def generate_array_with_size(size_mb, dtype=np.float32):\n",
+    "    size_bytes = size_mb * 1024 * 1024\n",
+    "    element_size = np.dtype(dtype).itemsize\n",
+    "    num_elements = size_bytes // element_size\n",
+    "    array = np.zeros(num_elements, dtype=dtype)\n",
+    "    return array\n",
+    "\n",
+    "big_array = generate_array_with_size(size_mb=1000)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Enable marshalling detailed report for each step."
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "%env MARSHALLING_DETAILED_REPORT=1"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Run cell with Score-P"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "%%execute_with_scorep\n",
+    "big_array\n",
+    "time.sleep(4)"
+   ]
   }
  ],
  "metadata": {