Add multilingual language and systems learning tracks

en/expanded-30-3/README.md

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+# CPU/GPU Core & Memory Mapping · 3
+
+[Agila University track](https://edu.agilaos.com/track.php?track=30&lang=en)
+
+About 15 minutes per lesson. Predict, run, change, explain, then complete the check in University.
+
+- [Lesson 1: GPU buffers and transfer cost](lesson-1/lesson.html)
+- [Lesson 2: Coalescing and shared workgroup memory](lesson-2/lesson.html)
+- [Lesson 3: Synchronization and buffer ownership](lesson-3/lesson.html)

en/expanded-30-3/lesson-1/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>GPU buffers and transfer cost</title><h1>GPU buffers and transfer cost</h1><p>15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.</p><p>Discrete GPUs often have device memory separate from host RAM. Uploads, downloads, launch overhead, and synchronization can dominate a small kernel. Unified memory changes the API but does not make movement free. Pinned host memory is a limited resource. The sample uses explicitly hypothetical timings; it illustrates accounting, not hardware performance.</p><p><strong>Practice and evidence:</strong> Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.</p><pre><code class="language-python">cpu_ms=8
+upload_ms,kernel_ms,download_ms=5,2,4
+print(&quot;Hypothetical GPU total:&quot;,upload_ms+kernel_ms+download_ms)
+print(&quot;Hypothetical CPU total:&quot;,cpu_ms)
+</code></pre><p><strong>Expected result:</strong> <code>Hypothetical GPU total: 11
+Hypothetical CPU total: 8</code></p><h3>Practice and evidence</h3><p>Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.</p><p><a href="https://git.agilaos.com/matt/open_agx">Language reference: git.agilaos.com</a></p><p><a href="https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/">Language reference: docs.nvidia.com</a></p></html>

en/expanded-30-3/lesson-1/main.py

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+cpu_ms=8
+upload_ms,kernel_ms,download_ms=5,2,4
+print("Hypothetical GPU total:",upload_ms+kernel_ms+download_ms)
+print("Hypothetical CPU total:",cpu_ms)

en/expanded-30-3/lesson-2/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>Coalescing and shared workgroup memory</title><h1>Coalescing and shared workgroup memory</h1><p>15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.</p><p>GPU lanes accessing adjacent elements can allow efficient memory transactions. Workgroup-shared memory may reduce repeated global reads, but it consumes a limited resource and requires correct synchronization. Names and details vary across CUDA, Metal, Vulkan, and other APIs. Draw which lane reads which element before changing data layout; validate the output after every layout change.</p><p><strong>Practice and evidence:</strong> Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.</p><pre><code class="language-python">lanes=4
+contiguous=[lane for lane in range(lanes)]
+strided=[lane*16 for lane in range(lanes)]
+print(contiguous)
+print(strided)
+</code></pre><p><strong>Expected result:</strong> <code>[0, 1, 2, 3]
+[0, 16, 32, 48]</code></p><h3>Practice and evidence</h3><p>Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.</p><p><a href="https://git.agilaos.com/matt/open_agx">Language reference: git.agilaos.com</a></p><p><a href="https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/">Language reference: docs.nvidia.com</a></p></html>

en/expanded-30-3/lesson-2/main.py

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+lanes=4
+contiguous=[lane for lane in range(lanes)]
+strided=[lane*16 for lane in range(lanes)]
+print(contiguous)
+print(strided)

en/expanded-30-3/lesson-3/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>Synchronization and buffer ownership</title><h1>Synchronization and buffer ownership</h1><p>15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.</p><p>Submitting a GPU job may return before execution finishes. Reusing or freeing its buffers too early can corrupt results. Model allocated, uploaded, submitted, completed, and released states. Require completion before reuse. A barrier has a defined scope; a workgroup barrier is not automatically synchronization between every workgroup or with the host.</p><p><strong>Practice and evidence:</strong> Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.</p><pre><code class="language-python">state=&quot;submitted&quot;
+def can_release(state): return state==&quot;completed&quot;
+assert not can_release(state)
+state=&quot;completed&quot;
+assert can_release(state)
+print(&quot;Passed&quot;)
+</code></pre><p><strong>Expected result:</strong> <code>Passed</code></p><h3>Practice and evidence</h3><p>Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.</p><p><a href="https://git.agilaos.com/matt/open_agx">Language reference: git.agilaos.com</a></p><p><a href="https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/">Language reference: docs.nvidia.com</a></p></html>

en/expanded-30-3/lesson-3/main.py

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+state="submitted"
+def can_release(state): return state=="completed"
+assert not can_release(state)
+state="completed"
+assert can_release(state)
+print("Passed")