Merge branch 'buildkite' of https://github.com/madeline-underwood/arm-learning-paths into buildkite

Author: madeline-underwood

.wordlist.txt

@@ -5039,4 +5039,19 @@ vesion
 wwwrun
 xdebug
 zoneIdentifier
-zypper
+zypper
+keyspace
+Keyspace
+CQL
+cqlsh
+keyspaces
+CQLSH
+SAI
+SSTables
+Trie
+UCS
+memtables
+cassandra
+Cassandra's
+Cassandra
+CircleCI

content/learning-paths/cross-platform/dynamic-memory-allocator/_index.md

@@ -5,7 +5,6 @@ minutes_to_complete: 120
 
 who_is_this_for: This is an introductory topic for software developers learning about dynamic memory allocation for the first time, and who may have used malloc and free in C programming. It also provides a starting point to explore more advanced memory allocation topics.
 
-layout: learningpathall
 learning_objectives:
 - Explain how dynamic memory allocation and the C heap works
 - Write a simple dynamic memory allocator
@@ -56,4 +55,4 @@ shared_between:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
----
+---

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started.md

@@ -21,7 +21,7 @@ This section walks you through the required tools and two supported setup option
 
 ## Download and explore the code examples
 
-To get started, begin by [downloading the code examples](https://gitlab.arm.com/learning-code-examples/code-examples/-/archive/main/code-examples-main.tar.gz?path=learning-paths/cross-platform/multiplying-matrices-with-sme2).
+To get started, begin by [downloading the code examples](https://gitlab.arm.com/learning-code-examples/code-examples/-/archive/d41190c0cf962f778ae71b94adf5330033019aed/code-examples-d41190c0cf962f778ae71b94adf5330033019aed.tar.gz?path=learning-paths/cross-platform/multiplying-matrices-with-sme2).
 
 Now extract the archive, and change directory to:
 ``code-examples/learning-paths/cross-platform/multiplying-matrices-with-sme2.``
@@ -68,7 +68,7 @@ Among other files, it includes:
 - `run-fvp.sh` to run the FVP model.
 - A `docker` directory containing:
   - `assets.source_me` to provide toolchain paths.
-  - `build-my-container.sh`, a script that automates building the Docker image from the `sme2-environment.docker` file. It runs the Docker build command with the correct arguments so you don’t have to remember them. 
+  - `build-my-container.sh`, a script that automates building the Docker image from the `sme2-environment.docker` file. It runs the Docker build command with the correct arguments so you don’t have to remember them.
   - `sme2-environment.docker`, a custom Docker file that defines the steps to build the SME2 container image. It installs all the necessary dependencies, including the SME2-compatible compiler and Arm FVP emulator.
   - `build-all-containers.sh`, a script to build multi-architecture images.
 - `.devcontainer/devcontainer.json` for VS Code container support.
@@ -113,7 +113,7 @@ If your machine doesn't support SME2, or you want to emulate it, you can use the
 
 The Docker container includes both a compiler and [Arm's Fixed Virtual Platform (FVP)
 model](https://developer.arm.com/Tools%20and%20Software/Fixed%20Virtual%20Platforms)
-for emulating code that uses SME2 instructions. You can either run the prebuilt container image provided in this Learning Path or build it yourself using the Docker file that is included. 
+for emulating code that uses SME2 instructions. You can either run the prebuilt container image provided in this Learning Path or build it yourself using the Docker file that is included.
 
 If building manually, follow the instructions in the ``sme2-environment.docker`` file to install the required tools on your machine.
 
@@ -144,7 +144,7 @@ that it is working with the following:
 docker run hello-world
 Unable to find image 'hello-world:latest' locally
 latest: Pulling from library/hello-world
-c9c5fd25a1bd: Pull complete 
+c9c5fd25a1bd: Pull complete
 Digest: sha256:940c619fbd418f9b2b1b63e25d8861f9cc1b46e3fc8b018ccfe8b78f19b8cc4f
 Status: Downloaded newer image for hello-world:latest
 
@@ -257,3 +257,12 @@ These Apple devices support SME2 natively.
 | Mac Mini (2024)                     | 2024         | M4, M4 Pro, M4 Max        |
 | MacBook Pro (14-inch, 16-inch, 2024)| 2024         | M4 Pro, M4 Max            |
 | MacBook Air (2025)                  | 2025         | M4                        |
+
+
+These Android phones support SME2 natively.
+
+
+| Device                              | Release Date | Chip Options              |
+|-------------------------------------|--------------|---------------------------|
+| Vivo X300                           | 2025         | MediaTek Dimensity 9500 featuring an 8-core Arm C1 CPU cluster and Arm G1-Ultra GPU |
+| OPPO Find X9                        | 2025         | MediaTek Dimensity 9500 featuring an 8-core Arm C1 CPU cluster and Arm G1-Ultra GPU |

content/learning-paths/embedded-and-microcontrollers/tfm/_index.md

@@ -58,6 +58,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
-layout: learningpathall
 
 ---

content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md

@@ -53,6 +53,5 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
-layout: learningpathall
 
 ---

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/1-install-plugin.md

@@ -35,7 +35,8 @@ Before you begin, download the required plugins and dependencies. These two repo
 
 [**Neural Super Sampling Unreal Engine Plugin** → GitHub Repository](https://github.com/arm/neural-graphics-for-unreal)
 
-Download the latest release package and extract it on your Windows machine.
+Download the latest release package and extract it on your Windows machine. Use the folder corresponding to your Unreal version.
+
 
 ### 2. Download the runtime for ML Extensions for Vulkan
 [**Unreal NNE Runtime RDG for ML Extensions for Vulkan** → GitHub Repository](https://github.com/arm/ml-extensions-for-vulkan-unreal-plugin).

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md

@@ -65,17 +65,30 @@ By default, Unreal uses DirectX. Instead, you need to choose Vulkan as the defau
 
 ![Project Settings with Vulkan selected as Default RHI under Targeted RHIs#center](./images/targeted_rhis.png "Figure 4: Set Vulkan as the default RHI.")
 
+## Create the Plugins directory
 
-## Add and enable the plugins
+Open your project directory in Windows explorer, and create a new folder called `Plugins`.
 
-1. Open your project directory in Windows explorer, and create a new folder called `Plugins`.
-2. Copy the downloaded and extracted `.zips` into the new directory:
+Enabling the plugin will look slightly different depending on what Unreal version you are using. Follow the steps corresponding to your setup.
+
+## For Unreal 5.5
+
+1. Copy the downloaded and extracted `.zip` into the new `Plugins` directory:
+   - `UE5.5`
    - `NNERuntimeRDGMLExtensionsForVulkan`
-   - `NSS`
-3. Re-open Unreal Engine. When prompted, confirm plugin integration.
-4. Rebuild your project in Visual Studio from source.
-5. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for both `NSS` and `NNERuntimeRDGMLExtensionsForVulkan` as shown. Restart Unreal Engine if prompted.
+2. Re-open Unreal Engine. When prompted, confirm plugin integration.
+3. Rebuild your project in Visual Studio from source.
+4. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for both `NSS` and `NNERuntimeRDGMLExtensionsForVulkan` as shown. Restart Unreal Engine if prompted.
 
 ![Unreal Engine plugins window showing NSS and NNERuntimeRDGMLExtensionsForVulkan enabled#center](./images/verify_plugin_enabled.png "Figure 5: Verify plugin installation in Unreal Engine.")
 
-With the emulation layers and plugins configured, you're ready to run Neural Super Sampling in Unreal Engine. Continue to the next section to test the integration.
+
+## For Unreal 5.4
+
+1. Copy the downloaded and extracted `.zip` into the new `Plugins` directory:
+   - `UE5.4`
+2. Re-open Unreal Engine. When prompted, confirm plugin integration.
+3. Rebuild your project in Visual Studio from source.
+4. Verify the installation by opening the Plugins view in Unreal Engine, and making sure the checkbox is selected for `NSS`. Restart Unreal Engine if prompted.
+
+With the emulation layers and plugins configured, you're ready to run Neural Super Sampling in Unreal Engine. Continue to the next section to test the integration.

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/_index.md

@@ -15,7 +15,7 @@ learning_objectives:
 
 prerequisites:
     - Windows 11
-    - Unreal Engine 5.5 (Templates and Feature Pack enabled)
+    - Unreal Engine 5.4 or 5.5 (Templates and Feature Pack enabled)
     - Visual Studio 2022 (with Desktop Development with C++ and .NET desktop build tools)
 
 

content/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_index.md

@@ -46,5 +46,4 @@ further_reading:
 weight: 1                       # _index.md always has weight of 1 to order correctly
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
-layout: learningpathall
 ---

content/learning-paths/servers-and-cloud-computing/cassandra-on-gcp/benchmnarking.md

@@ -1,15 +1,15 @@
 ---
-title: Cassendra Benchmarking
+title: Cassandra Benchmarking
 weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Cassendra Benchmarking by Cassendra-Stress
+## Cassandra Benchmarking by Cassandra-Stress
 Cassandra benchmarking can be performed using the built-in `cassandra-stress` tool, which helps measure database performance under different workloads such as write, read, and mixed operations.
 
-### Steps for Cassendra Benchmarking with Cassendra-Stress
+### Steps for Cassandra Benchmarking with Cassandra-Stress
 **Verify cassandra-stress Installation:**
 
 Cassandra comes with a built-in tool called **cassandra-stress** that is used for testing performance. It is usually located in the `tools/bin/` folder of your Cassandra installation. 
@@ -356,7 +356,7 @@ Results from the earlier run on the `c4a-standard-4` (4 vCPU, 16 GB memory) Arm6
 | Total GC Time               | 0.0 s                | 0.0 s                |
 | Total Operation Time        | 0:00:00              | 0:00:02              |
 
-### Cassendra performance benchmarking notes
+### Cassandra performance benchmarking notes
 When examining the benchmark results, you will notice that on the Google Axion C4A Arm-based instances:
 
 - The write operations achieved a high throughput of **10,690 op/s**, while read operations reached **4,962 op/s** on the `c4a-standard-4` Arm64 VM.