Move PyHPC profiling notebooks after notebook 02

docs/cscs.md

@@ -183,10 +183,10 @@ CSCS_EDF="${CSCS_EDF}" \
 The driver runs:
 
 - package smoke tests through the normal tutorial entrypoint
-- the full notebook ladder, including `03__mpi4py`
+- the full notebook ladder, including `06__mpi4py`
 - direct `nsys` and `ncu` command-line smoke checks
 
-The PyHPC image builds `mpi4py` against MPICH. Notebook 03 runs local ranks with
+The PyHPC image builds `mpi4py` against MPICH. Notebook 06 runs local ranks with
 `mpirun.mpich -launcher fork`, avoiding nested use of the host `srun` launcher.
 
 For debugging, the individual commands are below.
@@ -214,7 +214,7 @@ Run the profiling notebooks:
 ```bash
 srun -A "${CSCS_ACCOUNT}" -p normal -t 01:00:00 -N1 -n1 \
   --environment="${CSCS_EDF}" \
-  env ACH_RUN_TESTS=1 ACH_TEST_ARGS="10 or 11 or 12" \
+  env ACH_RUN_TESTS=1 ACH_TEST_ARGS="03 or 04 or 05" \
   /accelerated-computing-hub/brev/entrypoint.bash base
 ```
 

tutorials/pyhpc/.gitignore

@@ -1,12 +1,12 @@
 # Artifacts generated when the tutorial is run.
 #
-# SWE ladder (notebooks 04-09):
+# SWE ladder (notebooks 07-12):
 notebooks/build/
 notebooks/CMakeLists.txt
 notebooks/swe_step.cpython-*.so
 notebooks/timings.json
 
-# Kernel / asynchrony notebooks (10-12): %%writefile scripts, Nsight
+# Kernel / asynchrony notebooks (03-05): %%writefile scripts, Nsight
 # profiler reports, the downloaded text corpus, and rendered plots.
 # Patterns are applied at the notebooks/ root and under solutions/, since
 # executing the solution notebooks (e.g. in CI) writes the same artifacts there.

tutorials/pyhpc/README.md

@@ -17,7 +17,7 @@ Brev Launchables of this tutorial should use:
 
 ## Notebooks
 
-Each exercise notebook that has a paired solution carries `# TODO:` cells with `...` placeholders; the solution fills them in. The intro/reference notebook (04), the synthesis notebook (09), and the mpi4py walkthrough (03) are complete as written and have no separate solution.
+Each exercise notebook that has a paired solution carries `# TODO:` cells with `...` placeholders; the solution fills them in. The intro/reference notebook (07), the synthesis notebook (12), and the mpi4py walkthrough (06) are complete as written and have no separate solution.
 
 ### Fundamentals
 
@@ -27,32 +27,32 @@ Each exercise notebook that has a paired solution carries `# TODO:` cells with `
 | 01 | CuPy | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/01__cupy.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/01__cupy__SOLUTION.ipynb) |
 | 02 | Power Iteration - CuPy - Memory Spaces | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/02__power_iteration__cupy__memory_spaces.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/02__power_iteration__cupy__memory_spaces__SOLUTION.ipynb) |
 
-### Distributed
+### Kernels
 
 | # | Notebook | Link | Solution |
 |---|----------|------|----------|
-| 03 | mpi4py | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/03__mpi4py.ipynb) |  |
-
-### Programming models and interoperability
+| 03 | Power Iteration - CuPy - Asynchrony | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/03__power_iteration__cupy__asynchrony.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/03__power_iteration__cupy__asynchrony__SOLUTION.ipynb) |
+| 04 | Copy - Kernel Authoring | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/04__copy__kernel_authoring.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/04__copy__kernel_authoring__SOLUTION.ipynb) |
+| 05 | Book Histogram - Kernel Authoring | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/05__book_histogram__kernel_authoring.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/05__book_histogram__kernel_authoring__SOLUTION.ipynb) |
 
-Notebooks 04-09 are the Shallow Water Equations "ladder": an intro plus NumPy baseline, four solvers that each re-implement the same timestep with a different tool, and a synthesis that reads the measured timings and compares them. The synthesis notebook collects the per-tool rows from `timings.json` (written by notebooks 04 to 08) and closes with a matched-precision GPU comparison of the JAX and CppJIT device paths.
+### Distributed
 
 | # | Notebook | Link | Solution |
 |---|----------|------|----------|
-| 04 | SWE - Intro | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/04__swe__intro.ipynb) |  |
-| 05 | SWE - JAX | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/05__swe__jax.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/05__swe__jax__SOLUTION.ipynb) |
-| 06 | SWE - PyOMP | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/06__swe__pyomp.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/06__swe__pyomp__SOLUTION.ipynb) |
-| 07 | SWE - nanobind | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/07__swe__nanobind.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/07__swe__nanobind__SOLUTION.ipynb) |
-| 08 | SWE - CppJIT - Thrust | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/08__swe__cppjit__thrust.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/08__swe__cppjit__thrust__SOLUTION.ipynb) |
-| 09 | SWE - Synthesis | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/09__swe__synthesis.ipynb) |  |
+| 06 | mpi4py | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/06__mpi4py.ipynb) |  |
 
-### Kernels
+### Programming models and interoperability
+
+Notebooks 07-12 are the Shallow Water Equations "ladder": an intro plus NumPy baseline, four solvers that each re-implement the same timestep with a different tool, and a synthesis that reads the measured timings and compares them. The synthesis notebook collects the per-tool rows from `timings.json` (written by notebooks 07 to 11) and closes with a matched-precision GPU comparison of the JAX and CppJIT device paths.
 
 | # | Notebook | Link | Solution |
 |---|----------|------|----------|
-| 10 | Power Iteration - CuPy - Asynchrony | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/10__power_iteration__cupy__asynchrony.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/10__power_iteration__cupy__asynchrony__SOLUTION.ipynb) |
-| 11 | Copy - Kernel Authoring | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/11__copy__kernel_authoring.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/11__copy__kernel_authoring__SOLUTION.ipynb) |
-| 12 | Book Histogram - Kernel Authoring | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/12__book_histogram__kernel_authoring.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/12__book_histogram__kernel_authoring__SOLUTION.ipynb) |
+| 07 | SWE - Intro | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/07__swe__intro.ipynb) |  |
+| 08 | SWE - JAX | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/08__swe__jax.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/08__swe__jax__SOLUTION.ipynb) |
+| 09 | SWE - PyOMP | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/09__swe__pyomp.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/09__swe__pyomp__SOLUTION.ipynb) |
+| 10 | SWE - nanobind | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/10__swe__nanobind.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/10__swe__nanobind__SOLUTION.ipynb) |
+| 11 | SWE - CppJIT - Thrust | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/11__swe__cppjit__thrust.ipynb) | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/solutions/11__swe__cppjit__thrust__SOLUTION.ipynb) |
+| 12 | SWE - Synthesis | [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVIDIA/accelerated-computing-hub/blob/main/tutorials/pyhpc/notebooks/12__swe__synthesis.ipynb) |  |
 
 ### Python/C++ interoperability
 
@@ -64,7 +64,7 @@ A standalone comparison of several ways to call C and C++ from Python (ctypes, c
 
 ## The Shallow Water Equations problem
 
-A 1D shallow-water bump pulse: a small mound of water at rest splits into two outgoing wave packets. Two conserved fields (`h`, `hu`) advance under a forward-Euler step. This PDE is small enough to read in full while exhibiting nonlinearity, and a fixed number of steps from the initial condition gives a result we can compare across tools. The full specification is in [`04__swe__intro.ipynb`](./notebooks/04__swe__intro.ipynb).
+A 1D shallow-water bump pulse: a small mound of water at rest splits into two outgoing wave packets. Two conserved fields (`h`, `hu`) advance under a forward-Euler step. This PDE is small enough to read in full while exhibiting nonlinearity, and a fixed number of steps from the initial condition gives a result we can compare across tools. The full specification is in [`07__swe__intro.ipynb`](./notebooks/07__swe__intro.ipynb).
 
 ## Running
 
@@ -76,8 +76,8 @@ Locally, with an NVIDIA GPU and a CUDA-13-capable driver:
 docker compose -f tutorials/pyhpc/brev/docker-compose.yml up
 ```
 
-Then open JupyterLab on port 8888. The notebooks are self-contained and can be run in any order, with one exception: the Shallow Water Equations ladder writes `timings.json` as you run notebooks 04 to 08, so run those before the synthesis notebook (09).
+Then open JupyterLab on port 8888. The notebooks are self-contained and can be run in any order, with one exception: the Shallow Water Equations ladder writes `timings.json` as you run notebooks 07 to 11, so run those before the synthesis notebook (12).
 
 ## CppJIT toolchain
 
-Notebook 08 uses CppJIT to automatically bind our Python runtime with CUDA C++, using the clang-repl C++ interpreter and [CppInterOp](https://github.com/compiler-research/CppInterOp). This is currently source built in the [Docker image](./brev/dockerfile) and not a standard `pip install`, so this will only run in the tutorial image. [CppJIT](https://github.com/compiler-research/CppJIT) is the successor to the [cppyy](https://cppyy.readthedocs.io/) automatic bindings tool, and no official CppJIT release is published on PyPI yet (beta release planned for end of summer 2026).
+Notebook 11 uses CppJIT to automatically bind our Python runtime with CUDA C++, using the clang-repl C++ interpreter and [CppInterOp](https://github.com/compiler-research/CppInterOp). This is currently source built in the [Docker image](./brev/dockerfile) and not a standard `pip install`, so this will only run in the tutorial image. [CppJIT](https://github.com/compiler-research/CppJIT) is the successor to the [cppyy](https://cppyy.readthedocs.io/) automatic bindings tool, and no official CppJIT release is published on PyPI yet (beta release planned for end of summer 2026).

tutorials/pyhpc/brev/requirements.txt

@@ -12,22 +12,22 @@ jupyter-dark-detect == 0.1.0
 nbformat == 5.10.4
 nbconvert == 7.17.1
 
-# Programming models and interop (notebooks 04-09: the SWE ladder)
+# Programming models and interop (notebooks 07-12: the SWE ladder)
 jax[cuda13] == 0.10.0
 pyomp == 0.5.1
 nanobind == 2.12.0
 pybind11 == 3.0.4
 
-# CuPy and CUDA kernels (notebooks 00-02, 10-12: ported accelerated-python exercises)
+# CuPy and CUDA kernels (notebooks 00-05: ported accelerated-python exercises)
 cupy-cuda13x == 13.6.0
 numba-cuda[cu13] == 0.28.2
 cuda-cccl[test-cu13] == 0.4.5
 
-# NVIDIA developer tools (notebooks 10-12: Nsight Systems / Nsight Compute profiling)
+# NVIDIA developer tools (notebooks 03-05: Nsight Systems / Nsight Compute profiling)
 nvtx == 0.2.14
 nsightful[notebook] @ git+https://github.com/brycelelbach/nsightful.git@454dfa5124ebea193f64302058b327f2034ccd8e
 
-# Distributed computing (notebook 03: mpi4py)
+# Distributed computing (notebook 06: mpi4py)
 mpi4py == 4.1.1
 
 # Python/C++ Interop (notebook 13)

tutorials/pyhpc/notebooks/04__swe__intro.ipynb → tutorials/pyhpc/notebooks/07__swe__intro.ipynb

@@ -440,12 +440,12 @@
     "\n",
     "| NB | Tool | Programming-model hook |\n",
     "|---|---|---|\n",
-    "| 05 | **JAX** | JIT compilation + whole-loop fusion offloaded to device |\n",
-    "| 06 | **PyOMP** | OpenMP parallelisation model in Python |\n",
-    "| 07 | **nanobind** | Handwritten Python bindings for compiled C++ kernels |\n",
-    "| 08 | **CppJIT** | JIT compiled C++ with automatic bindings |\n",
+    "| 08 | **JAX** | JIT compilation + whole-loop fusion offloaded to device |\n",
+    "| 09 | **PyOMP** | OpenMP parallelisation model in Python |\n",
+    "| 10 | **nanobind** | Handwritten Python bindings for compiled C++ kernels |\n",
+    "| 11 | **CppJIT** | JIT compiled C++ with automatic bindings |\n",
     "\n",
-    "We end with `09__swe__synthesis.ipynb`: a decision matrix and the achieved-throughput plot across the different notebooks in `timings.json`."
+    "We end with `12__swe__synthesis.ipynb`: a decision matrix and the achieved-throughput plot across the different notebooks in `timings.json`."
    ]
   },
   {
@@ -455,7 +455,7 @@
    "source": [
     "### <a id=\"sec10\"></a>10. How to read these notebooks\n",
     "\n",
-    "Each numbered exercise (05-08) comes as two files:\n",
+    "Each numbered exercise (08-11) comes as two files:\n",
     "\n",
     "- `NN__topic__subtopic.ipynb`: the primary notebook we run live during the tutorial, with `# TODO:` cells whose body is a `...` placeholder.\n",
     "- `solutions/NN__topic__subtopic__SOLUTION.ipynb`: notebooks complete with solution, for self-paced review.\n",
@@ -465,7 +465,7 @@
     "- A **Quick Docs** sub-block precedes each TODO cell, listing the key APIs you need.\n",
     "- Optional **`# BONUS TASK:`** cells explore deeper solutions if you have time.\n",
     "\n",
-    "With the NumPy baseline in hand, let's continue with JAX: `05__swe__jax.ipynb`"
+    "With the NumPy baseline in hand, let's continue with JAX: `08__swe__jax.ipynb`"
    ]
   }
  ],

tutorials/pyhpc/notebooks/05__swe__jax.ipynb → tutorials/pyhpc/notebooks/08__swe__jax.ipynb

@@ -85,7 +85,7 @@
    "source": [
     "### <a id=\"sec2\"></a>2. The step as a pure function\n",
     "\n",
-    "Let's look at the same operation shape as notebook 04, expressed in JAX:\n",
+    "Let's look at the same operation shape as notebook 07, expressed in JAX:\n",
     "\n",
     "- **Read** — `h[:-1]`, `h[1:]` for interface states, same as NumPy-style slicing.\n",
     "- **Compute** — Rusanov formula unchanged: `F_h = 0.5*(huL+huR) - 0.5*a*(hR-hL)`.\n",

tutorials/pyhpc/notebooks/06__swe__pyomp.ipynb → tutorials/pyhpc/notebooks/09__swe__pyomp.ipynb

@@ -400,7 +400,7 @@
     "- Thread scaling works (section 5), and the kernel handles any `N`\n",
     "  with no recompile (section 6).\n",
     "\n",
-    "Next: `07__swe__nanobind.ipynb` drops into C++. We compile a\n",
+    "Next: `10__swe__nanobind.ipynb` drops into C++. We compile a\n",
     "hand-written kernel and call it from Python through a binding library.\n"
    ]
   }