Add multilingual language and systems learning tracks

en/expanded-30-4/README.md

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+# CPU/GPU Core & Memory Mapping · 4
+
+[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: Measure repeated runs with a real clock](lesson-1/lesson.html)
+- [Lesson 2: Select a backend from evidence](lesson-2/lesson.html)
+- [Lesson 3: Publish a reproducible placement study](lesson-3/lesson.html)

en/expanded-30-4/lesson-1/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>Measure repeated runs with a real clock</title><h1>Measure repeated runs with a real clock</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>Use a monotonic high-resolution clock. Warm up separately, repeat a fixed workload, verify a checksum, and report median plus spread. Record units, data size, environment, and compiler/runtime version. The following measures Python sum on this CPU and includes interpreter/runtime costs; it is not a GPU or raw memory-bandwidth benchmark.</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">import time,statistics,platform
+data=list(range(100000))
+expected=4999950000
+assert sum(data)==expected
+samples=[]
+for _ in range(7):
+    start=time.perf_counter_ns()
+    result=sum(data)
+    samples.append((time.perf_counter_ns()-start)/1e6)
+    assert result==expected
+print(&quot;CPU Python&quot;,platform.python_version(),platform.machine())
+print(&quot;median_ms&quot;,round(statistics.median(samples),4))
+print(&quot;min_ms&quot;,round(min(samples),4),&quot;max_ms&quot;,round(max(samples),4))
+</code></pre><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-30-4/lesson-1/main.py

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+import time,statistics,platform
+data=list(range(100000))
+expected=4999950000
+assert sum(data)==expected
+samples=[]
+for _ in range(7):
+    start=time.perf_counter_ns()
+    result=sum(data)
+    samples.append((time.perf_counter_ns()-start)/1e6)
+    assert result==expected
+print("CPU Python",platform.python_version(),platform.machine())
+print("median_ms",round(statistics.median(samples),4))
+print("min_ms",round(min(samples),4),"max_ms",round(max(samples),4))

en/expanded-30-4/lesson-2/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>Select a backend from evidence</title><h1>Select a backend from evidence</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>Filter candidates by capability, correctness, memory capacity, and constraints before comparing speed. Match representative workload sizes and include transfers. Preserve a fallback when a device is absent. The table below contains invented demonstration results, explicitly marked as such; replace them with real measurements only after validation. Do not fit a policy to one lucky run.</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">candidates=[{&quot;name&quot;:&quot;CPU demo&quot;,&quot;correct&quot;:True,&quot;ms&quot;:8},{&quot;name&quot;:&quot;GPU demo&quot;,&quot;correct&quot;:True,&quot;ms&quot;:11},{&quot;name&quot;:&quot;Broken demo&quot;,&quot;correct&quot;:False,&quot;ms&quot;:1}]
+valid=[c for c in candidates if c[&quot;correct&quot;]]
+print(&quot;Hypothetical winner:&quot;,min(valid,key=lambda c:c[&quot;ms&quot;])[&quot;name&quot;])
+</code></pre><p><strong>Expected result:</strong> <code>Hypothetical winner: CPU demo</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-30-4/lesson-2/main.py

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+candidates=[{"name":"CPU demo","correct":True,"ms":8},{"name":"GPU demo","correct":True,"ms":11},{"name":"Broken demo","correct":False,"ms":1}]
+valid=[c for c in candidates if c["correct"]]
+print("Hypothetical winner:",min(valid,key=lambda c:c["ms"])["name"])

en/expanded-30-4/lesson-3/lesson.html

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+<!doctype html><html lang="en"><meta charset="utf-8"><title>Publish a reproducible placement study</title><h1>Publish a reproducible placement study</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>Build a report for at least two workload sizes. Include source revision, device topology, memory type and placement, affinity, input seed, correctness tolerance, repetitions, raw samples, and end-to-end cost. Distinguish measured results, simulations, and untested candidates. Explain why the winning policy may change on another machine. Submit code and raw evidence on Forge, never confidential machine credentials.</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><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>