Typos and tidy up

Author: liamjberrisford

_config.yml

@@ -39,11 +39,4 @@ html:
       # table-width: 100%       # (optional) stretch tables too
 
 execute:
-  execute_notebooks: "force"
-  exclude_patterns: 
-      - 'lessons/theory.ipynb'
-      - 'lessons/spack.ipynb'
-      - 'lessons/slurm.ipynb'
-      - 'lessons/conways_game_of_life.ipynb'
-      - 'lessons/profiling.ipynb'
-      - 'lessons/temperature_diffusion.ipynb'
+  execute_notebooks: "force"

lessons/conways_game_of_life.ipynb

@@ -454,28 +454,6 @@
     "### Finer Grained Timing Information\n",
     "Our next step is to quantify those differences by measuring exactly how long each stage takes (pure computation, data transfers, grid initialisation, etc.) and to pinpoint where the bulk of the time is spent. The following section will address these questions by introducing profiling techniques."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c278feb1-bfad-4799-b21a-f3c9c922e2a3",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f2165de-735a-46d9-867f-3a58fa2e8007",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

lessons/profiling.ipynb

@@ -511,18 +511,6 @@
     "```"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "03db6331-fff6-4a1c-ba7c-c83133e119b9",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "c2187d99-e201-495c-a234-49ac50ca9317",
@@ -550,20 +538,6 @@
     "- **Memory access patterns**: This is more advanced, but if diving into custom kernel, coalesced memory access (accessing consecutive memory addresses in threads that are next to each other) is important for performance. Uncoalesced or random access can slow down even if arithmetic is small.\n",
     "- **Use specialised libraries**: For certain tasks, libraries like cuDNN (deep neural nets), cuBLAS (linear algebra), etc., are heavily optimised. Always prefer a library call (e.g., `cp.fft` or `cp.linalg`) over writing your own, if it fits the need, because those are likely tuned for performance.\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e015244d-0653-4b91-bbee-b51be8eec8a7",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

lessons/slurm.ipynb

@@ -70,7 +70,7 @@
     "$ squeue -u your_username\n",
     "```\n",
     "\n",
-    "This shows all your jobs (use `squeue` alone to see everyon's jobs, but that can be long on busy systems. Typical `squeue` output columns include: \n",
+    "This shows all your jobs (use `squeue` alone to see everyone's jobs, but that can be long on busy systems. Typical `squeue` output columns include: \n",
     "- **JOBID**: The job ID (e.g. 123456) \n",
     "- **PARTITION**: Which partition/queue is it in?.\n",
     "- **NAME**: the job name. \n",