Deploy Rust on Google Cloud C4A (Arm-based Axion VMs)

odidev · odidev · commit f275dcbb59b3 · 2025-11-13T05:30:41.000Z
Signed-off-by: odidev &lt;odidev@puresoftware.com&gt;
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_index.md
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+---
+title: Deploy Rust on Google Cloud C4A (Arm-based Axion VMs)
+
+minutes_to_complete: 30
+
+who_is_this_for: This learning path is intended for software developers deploying and optimizing Rust workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by Axion processors. 
+
+learning_objectives:
+  - Provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors)
+  - Install Rust and configure the development environment on a SUSE Arm64 (C4A) instance
+  - Verify Rust setup by compiling and running a sample program to ensure toolchain functionality 
+  - Benchmark Rust using cargo bench with Criterion to measure execution speed, stability, and performance on Arm64 systems
+
+prerequisites:
+  - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
+  - Basic familiarity with [Rust](https://www.rust-lang.org/)
+
+author: Pareena Verma
+
+##### Tags
+skilllevels: Introductory
+subjects: Performance and Architecture
+cloud_service_providers: Google Cloud
+
+armips:
+  - Neoverse
+
+tools_software_languages:
+  - Rust
+  - Cargo
+  - Criterion
+
+operatingsystems:
+  - Linux
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+further_reading:
+  - resource:
+      title: Google Cloud documentation
+      link: https://cloud.google.com/docs
+      type: documentation
+
+  - resource:
+      title: Rust documentation
+      link: https://doc.rust-lang.org/stable/
+      type: documentation
+  
+  - resource:
+      title: Cargo bench documentation
+      link: https://doc.rust-lang.org/cargo/commands/cargo-bench.html
+      type: documentation   
+
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: "yes"
+---
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/_next-steps.md
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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/background.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/background.md
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+---
+title: Getting started with Rust on Google Axion C4A (Arm Neoverse-V2)
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Google Axion C4A Arm instances in Google Cloud
+
+Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as CI/CD pipelines, microservices, media processing, and general-purpose applications.
+
+The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability and performance benefits of the Arm architecture in Google Cloud.
+
+To learn more about Google Axion, refer to the [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu) blog.
+
+## Rust
+
+[Rust](https://www.rust-lang.org/) is a modern, high-performance systems programming language designed for safety, speed, and concurrency. It provides memory safety without garbage collection, making it ideal for building reliable and efficient software.
+
+Developed by Mozilla, Rust is widely used in system-level programming, web assembly, embedded systems, and performance-critical applications.  
+Its strong type system and ownership model help prevent common bugs like data races and memory leaks.
+
+To learn more, visit the [official Rust website](https://www.rust-lang.org/).
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/baseline.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/baseline.md
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+---
+title: Rust Baseline Testing on Google Axion C4A Arm Virtual Machine
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Rust Baseline Testing on GCP SUSE VMs
+This guide demonstrates how to perform baseline testing of Rust on GCP SUSE Arm64 VMs, verifying installation, build functionality, and compilation performance on the Arm-based Axion C4A platform.
+
+### Verify Installation
+Check the Rust version and toolchain setup:
+
+```console
+rustc --version
+cargo --version
+```
+
+### Create a Sample Rust Program
+Create and build a simple “Hello, World” application to ensure everything is functioning properly:
+
+```console
+mkdir rust-baseline
+cd rust-baseline
+cargo new hello
+cd hello
+cargo run
+```
+- **`mkdir rust-baseline`** – Creates a new directory named `rust-baseline`.  
+- **`cd rust-baseline`** – Enters the `rust-baseline` directory.  
+- **`cargo new hello`** – Creates a new Rust project named `hello` with default files (`main.rs`, `Cargo.toml`).  
+- **`cd hello`** – Moves into the newly created `hello` project directory.  
+- **`cargo run`** – Compiles and runs the Rust program, printing **“Hello, world!”**, confirming that Rust and Cargo are properly set up.
+
+You should see an output similar to:
+```output
+   Compiling hello v0.1.0 (/home/gcpuser/rust-baseline/hello)
+    Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.19s
+     Running `target/debug/hello`
+Hello, world!
+```
+
+### Measure Compilation Speed
+Use the time command to measure how long Rust takes to compile a small program:
+
+```console
+cargo clean
+time cargo build
+```
+This gives a rough idea of compilation performance on the Arm64 CPU.
+
+You should see an output similar to:
+```output
+Removed 21 files, 7.7MiB total
+   Compiling hello v0.1.0 (/home/gcpuser/rust-baseline/hello)
+    Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.14s
+
+real    0m0.186s
+user    0m0.118s
+sys     0m0.071s
+```
+This confirms that Rust is working properly on your Arm64 VM.
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/benchmarking.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/benchmarking.md
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+---
+title: Rust Benchmarking
+weight: 6
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+
+## Rust Benchmarking by cargo bench
+This section demonstrates how to benchmark Rust performance using **official Rust benchmarking tools** — `cargo bench` and the **Criterion** library — to measure code execution speed, stability, and performance consistency on Arm64 hardware.
+
+### Verify Rust and Cargo
+Ensure that Rust and Cargo are properly installed before running benchmarks
+
+### Create a New Rust Project
+Create a new Rust project for benchmarking:
+
+```console
+cargo new rust-benchmark
+cd rust-benchmark
+```
+### Add Criterion Benchmarking Dependency
+**Criterion** is the officially recommended benchmarking crate for Rust.Add it to your project by editing the `Cargo.toml` file:
+
+```toml
+[dependencies]
+criterion = "0.5"
+
+[[bench]]
+name = "my_benchmark"
+harness = false
+```
+This enables Criterion for high-precision benchmarking.
+
+### Create the Benchmark File
+Create a new benchmark file inside the `benches/` directory:
+
+```console
+mkdir benches
+vi benches/my_benchmark.rs
+```
+Benchmark files in this directory are automatically detected by Cargo.
+
+**Add the Benchmark Code**
+Paste the following benchmark code:
+
+```rust
+use criterion::{black_box, Criterion, criterion_group, criterion_main};
+
+// Example benchmark function
+fn fibonacci(n: u64) -> u64 {
+    match n {
+        0 => 0,
+        1 => 1,
+        n => fibonacci(n - 1) + fibonacci(n - 2),
+    }
+}
+
+fn benchmark_fibonacci(c: &mut Criterion) {
+    c.bench_function("fibonacci 20", |b| b.iter(|| fibonacci(black_box(20))));
+}
+
+criterion_group!(benches, benchmark_fibonacci);
+criterion_main!(benches);
+```
+This code measures how efficiently Rust computes the 20th Fibonacci number.
+
+### Run the Benchmark
+Now run the benchmark using Cargo:
+
+```console
+cargo bench
+```
+Cargo compiles your code in optimized mode and runs the Criterion benchmarks, showing execution time, performance deviation, and stability metrics for your Rust functions.
+
+You should see an output similar to:
+```output
+Running benches/my_benchmark.rs (target/release/deps/my_benchmark-f40a307ef9cad515)
+Gnuplot not found, using plotters backend
+fibonacci 20            time:   [12.026 µs 12.028 µs 12.030 µs]
+Found 1 outliers among 100 measurements (1.00%)
+  1 (1.00%) low mild
+```
+
+### Benchmark Metrics Explanation
+
+- **Average Time:** Mean execution time across benchmark runs.  
+- **Outliers:** Represent runs significantly slower or faster than average.  
+- **Plotting Backend:** Used `plotters` since Gnuplot was not found.  
+- The results show **consistent performance** with only slight variation across 100 measurements.
+
+### Benchmark summary on x86_64
+To compare the benchmark results, the following results were collected by running the same benchmark on a `x86 - c4-standard-4` (4 vCPUs, 15 GB Memory) x86_64 VM in GCP, running SUSE:
+
+| **Benchmark**     | **Average Time (µs)** | **Min (µs)** | **Max (µs)** | **Outliers (%)** | **Remarks**                     |
+|--------------------|----------------------:|--------------:|--------------:|-----------------:|----------------------------------|
+| **fibonacci 20**   | 19.152               | 19.100        | 19.205        | 6.00%            | Minor outliers, stable overall.  |
+
+### Benchmark summary on Arm64
+Results from the earlier run on the `c4a-standard-4` (4 vCPU, 16 GB memory) Arm64 VM in GCP (SUSE):
+
+| **Benchmark**     | **Average Time (µs)** | **Min (µs)** | **Max (µs)** | **Outliers (%)** | **Remarks**                     |
+|--------------------|----------------------:|--------------:|--------------:|-----------------:|----------------------------------|
+| **fibonacci 20**   | 12.028               | 12.026        | 12.030        | 1.00%            | Very stable performance, minimal variation. |
+
+### Rust benchmarking comparison on Arm64 and x86_64
+
+- The **Fibonacci (n=20)** benchmark demonstrated **consistent performance** with minimal deviation.  
+- **Average execution time** was around **12.028 µs**, indicating efficient CPU computation on **Arm64**.  
+- Only **1% outliers** were detected, showing **high stability** and **repeatability** of results.  
+- Overall, the test confirms **Rust’s reliable execution speed** and **low variance** on the Arm64 platform.
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/images/gcp-vm.png b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/images/gcp-vm.png
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/installation.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/installation.md
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+---
+title: Install Rust
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Rust Installation on GCP SUSE VM
+This guide explains how to install and configure Rust on a GCP SUSE Arm64 VM, ensuring the environment is ready for building and benchmarking Rust applications.
+
+### System Preparation
+Updates the system and installs essential build tools for compiling Rust programs.
+
+```console
+sudo zypper refresh
+sudo zypper update -y
+sudo zypper install -y curl gcc make
+```
+### Install Rust Using rustup
+Rust provides an official installer script via `rustup`, which handles the setup automatically:
+
+```console
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+```
+When prompted, you can choose **option 1** (default installation).
+
+### Configure Rust Environment
+Activates Rust’s environment variables for the current shell session.
+
+```console
+source $HOME/.cargo/env
+```
+
+### Verify Rust Installation
+Confirms successful installation of Rust and Cargo by checking their versions.
+
+```console
+rustc --version
+cargo --version
+```
+
+You should see an output similar to:
+```output
+rustc 1.91.0 (f8297e351 2025-10-28)
+cargo 1.91.0 (ea2d97820 2025-10-10)
+```
+Rust installation is complete. You can now go ahead with the baseline testing of Rust in the next section.
diff --git a/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/instance.md b/content/learning-paths/servers-and-cloud-computing/rust-on-gcp/instance.md
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+---
+title: Create a Google Axion C4A Arm virtual machine on GCP 
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Overview
+
+In this section, you will learn how to provision a Google Axion C4A Arm virtual machine on Google Cloud Platform (GCP) using the `c4a-standard-4` (4 vCPUs, 16 GB memory) machine type in the Google Cloud Console.  
+
+{{% notice Note %}}
+For support on GCP setup, see the Learning Path [Getting started with Google Cloud Platform](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/google/).
+{{% /notice %}}
+
+## Provision a Google Axion C4A Arm VM in Google Cloud Console
+
+To create a virtual machine based on the C4A instance type:
+- Navigate to the [Google Cloud Console](https://console.cloud.google.com/).
+- Go to **Compute Engine > VM Instances** and select **Create Instance**. 
+- Under **Machine configuration**:
+   - Populate fields such as **Instance name**, **Region**, and **Zone**.
+   - Set **Series** to `C4A`.
+   - Select `c4a-standard-4` for machine type.
+
+   ![Create a Google Axion C4A Arm virtual machine in the Google Cloud Console with c4a-standard-4 selected alt-text#center](images/gcp-vm.png "Creating a Google Axion C4A Arm virtual machine in Google Cloud Console")
+
+- Under **OS and Storage**, select **Change**, then choose an Arm64-based OS image. For this Learning Path, use **SUSE Linux Enterprise Server**. Pick the preferred version for your Operating System. Ensure you select the **Arm image** variant. Click **Select**.
+- Under **Networking**, enable **Allow HTTP traffic**.
+- Click **Create** to launch the instance.
