Using the Go Compiler on the Raspberry Pi?

The Raspberry Pi is a series of small, affordable single-board computers that have become wildly popular among hobbyists, educators, and developers. With a Raspberry Pi, you can build all sorts of creative projects from retro game consoles to home automation systems.

Using the Go Compiler on the Raspberry Pi?

One great use case for the Pi is as a platform for building applications with the Go programming language. Go is an open source language developed by Google that combines the ease of development of interpreted languages with the performance and efficiency of compiled languages.

In this comprehensive guide, you’ll learn how to set up your Raspberry Pi for Go development, build Go applications that run directly on the Pi, and cross-compile your Go code for other platforms. We’ll also cover optimization techniques to make your Go programs run as fast as possible on the Raspberry Pi’s limited hardware.


To follow along with this guide, you’ll need the following:

  • A Raspberry Pi board. Any model will work, but the 3B+ or 4 provides the best performance.
  • A microSD card loaded with a recent version of Raspberry Pi OS
  • Basic peripherals for your Pi like a power supply, monitor, keyboard, etc.
  • Network connectivity for your Pi via Ethernet or WiFi.
  • Go installed on your main development machine (not the Pi itself yet). You can get Go from the official website.

No prior Go experience is required to follow along, but familiarity with Go’s syntax and basic constructs will be helpful.

Step 1 – Install Go on the Raspberry Pi

The first step is to get Go installed directly on your Raspberry Pi. Here are two options for installing Go:

Install Using apt

Raspberry Pi OS includes the apt package manager, which has a package for Go in its repositories:


sudo apt update

sudo apt install golang-go

This will install a recent but possibly outdated version of Go.

Install from Official Site

For the latest Go release, you can download directly from the official Go website:



sudo tar -C /usr/local -xzf go1.18.1.linux-armv6l.tar.gz

Make sure to change the version number to the latest stable release.

Either installation method will setup Go in /usr/local/go and add it to your PATH.

Step 2 – Set Up Your Workspace

Go developers typically keep all their Go source code in a single workspace. We need to set this up on the Pi:


mkdir ~/work

cd ~/work

export GOPATH=$(pwd)

export PATH=$PATH:$GOPATH/bin

Now your Go workspace is ready at ~/work. The GOPATH and PATH environment variables point the Go toolchain at this workspace.

Step 3 – Write a Simple Go Program

Let’s jump right in and write a simple Go program to print “Hello World” and run it on the Pi:


// main.go

package main

import “fmt”

func main() {

  fmt.Println(“Hello World on Raspberry Pi!”)


Save this as ~/work/src/main.go and run it:


go run ~/work/src/main.go

You should see the “Hello World” message print. This verifies our Go install is working properly.

Now let’s compile the program to make a native binary:


go build -o hello ~/work/src/main.go 


The compiled hello binary also prints “Hello World”.

Step 4 – Cross Compile Go for Other Platforms

A great feature of Go is the ability to cross compile your code for different operating systems and architectures. This allows you to develop on the Pi but generate binaries for another platform like Linux x86_64.

Cross compiling is as easy as setting the GOOS and GOARCH environment variables:


 Compile for Linux x86_64

GOOS=linux GOARCH=amd64 go build -o hello_linux main.go

 Compile for Windows x86_64

GOOS=windows GOARCH=amd64 go build -o hello_windows.exe main.go

Now you have Go binaries for Linux and Windows even though we only compiled them on the Pi.

Step 5 – Optimize Go Code for the Raspberry Pi

Although the Pi 4 is quite capable, it has limited processing power and memory compared to a modern laptop or desktop. To get the best performance out of our Go programs on the Pi, we need to carefully optimize our code.

Here are some key optimization techniques:

  • Avoid memory allocations: Try to minimize heap allocations and reuse buffers. The escape analysis and inlining capabilities of the Go compiler will often handle this for you. But pay close attention for easy improvements.
  • Preallocate slices and maps: Don’t append one item at a time to a slice. Preallocate with make() to avoid expensive grows.
  • Use Go routines wisely: Goroutines have little overhead, but they aren’t free. Try to limit goroutine usage in inner loops or performance critical paths.
  • Avoid reflection and boxing: Reflection and boxing interfaces require significant work. Avoid if possible.
  • Make fewer system calls: System calls have high latency on the Pi. Batch data when interacting with files and network.
  • Use Cgo judiciously: Avoid blocking Cgo calls. But Cgo can improve performance in some cases by avoiding overhead.
  • Optimize slow functions: The built-in testing and profiling tools make optimization easy. Measure, don’t guess!

Let’s look at some concrete examples to demonstrate these principles…

Avoid Unnecessary Allocations

This function allocates a new []byte on every call:


func concat(a, b string) string {

  c := make([]byte, len(a)+len(b))

  // …copy bytes

  return string(c) 

We can reuse a buffer instead:


var buffer bytes.Buffer 

func concat(a, b string) string {


  // …copy bytes

  return buffer.String()

Preallocate Slices

Appending to a slice with append() can cause expensive copy and reallocations. Pre-size your slices with make():



var s []int 

for _, v := range values {

  s = append(s, v)


s := make([]int, 0, len(values))

for _, v := range values {

  s = append(s, v)

Carefully Use Goroutines

Goroutines are great, but don’t overuse them. This silly example fires a goroutine for every loop iteration:


func sum(values []int) int {

  ch := make(chan int)

  for _, v := range values {

    go func() { 

      ch <- 

 collect results

  s := 0

  for range values {

    s += <-ch


Instead, do the work synchronously:


func sum(values []int) int {

  var s int

  for _, v := range values {

    s += v

  return s

Step 6 – Benchmark and Profile

To accurately optimize Go code, you need to measure and profile CPU and memory usage. The Go testing and runtime/pprof packages make this easy.

For example, we can benchmark a function:


func BenchmarkConcat(b *testing) {

  x := strings. Repeat(“a”, 100)

  y := strings.Repeat(“b”, 100)

  for i := 0; i < b.N; i++ {

    concat(x, y) 

Run benchmarks:

go test -bench .

The CPU profile shows where the program spends time:


import “runtime/pprof”


pprof.StartCPUProfile(w io.Writer) 

defer pprof.StopCPUProfile()

Memory profiles show allocation stats:


import “runtime/pprof” 


pprof.WriteHeapProfile(w io. Writer)

Always measure first, then optimize. Don’t guess where the bottlenecks are.

Step 7 – Share Packages on GitHub

Once you have some useful Go libraries and programs written, share them on GitHub so other Raspberry Pi developers can benefit from your work.

Make sure to include:

  • A explaining what your project does
  • Godoc formatted documentation comments
  • Examples and tests
  • MIT, BSD, or other permissive license

By open sourcing your projects, you contribute back to the Go community.

Key Takeaways

Here are the key things to remember about using Go on the Raspberry Pi:

  • Install Go directly on the Pi for native development.
  • Cross compile Go programs for other platforms.
  • Optimize memory usage and allocation to avoid paging.
  • Don’t overuse goroutines – keep them out of tight loops.
  • Preallocate slices, reuse buffers, and avoid boxing.
  • Use the built-in benchmarking and profiling tools.
  • Open source useful libraries and tools on GitHub.

Following these tips will let you build super fast Go applications that make the most of the Raspberry Pi’s capabilities.


The Raspberry Pi’s low cost and small size make it an ideal platform for learning and developing with Go. By installing Go directly on the device, cross-compiling Go programs for alternate platforms, and optimizing your code, you can build efficient applications even on the Pi’s limited hardware.

Go’s concurrency features and performance allow you to leverage the Pi’s quad-core ARM processor for tasks like web servers, robots, IoT devices, and more. And Go’s portability lets you develop on the Pi but deploy anywhere.

So set up a Raspberry Pi Go development environment and see the power of this combination! Let us know about the creative Go projects you come up with for the Pi by tweeting @Raspberry_Pi and @golang.

Frequently Asked Questions

Q: Which Raspberry Pi models work best for Go development?
A: The Pi 4 and Pi 3B+ have the best performance for Go due to their faster processors and more memory. But any Pi model can work.

Q: Can I use Go on a Raspberry Pi Pico or Pi Zero?
A: Yes, you can install Go on these smaller Pi boards, but performance will be slower due to their constraints on memory and processing power.

Q: How do I edit and compile Go code on a Pi?
A: Use any editor like Vim or VS Code over SSH. Compile code with the go build command. Use a tool like rsync to sync code to your Pi.

Q: Is there a Docker image with Go pre-installed for Raspberry Pi?
A: Yes, several Docker images like arm32v7/golang have Go ready to go for ARM based devices like the Pi.

Q: Can I cross-compile Go for the Pi from my desktop or laptop?
A: Absolutely. Set GOOS=linux and GOARCH=arm or the specific Pi processor architecture.

Q: Is there a way to speed up Go build times on the Pi?
A: Using go build -x -work will cache build artifacts and skip unchanged packages to significantly reduce compile times.

Q: How do I profile Go program CPU and memory usage on the Pi?
A: Use the testing package Benchmark functions and the runtime/pprof package for CPU and memory profiling.

Q: What kind of applications are best written in Go for the Raspberry Pi?
A: Go works great for network services, web servers, databases,scraping tools, robotics programs, automation tools, and IoT applications.

Q: Can I use Go for machine learning on the Raspberry Pi?
A: Yes, libraries like Go Learn provide machine learning capabilities, but training complex models is limited by the Pi’sconstrained hardware. Better for inferencing pre-trained models.

Q: What external libraries extend Go’s capabilities for the Pi?
A: gobot for robotics, raspi for GPIO access, mattn/go-sqlite3 for databases, d2r2 for machine learning, gio for desktop GUIs.

Q: Should I use Cgo or avoid it on the Pi?
A: Avoid Cgo if your program doesn’t need it. But used judiciously, Cgo can sometimes improve performance by avoiding overhead. Profile to find out.

Q: How can I make network programming faster in Go on the Pi?
A: Batch data when reading/writing across the network. Use pooling, buffer reuse, and other optimization techniques.

Q: What ARM instruction sets does the Pi support?
A: Original Pis are ARMv6. Newer models are ARMv7 and ARMv8 with capabilities like NEON SIMD instructions. Compile Go accordingly.

Q: Is Go a good teaching language to use with Raspberry Pi projects?
A: Yes! Go’s simplicity, built-in concurrency, and variety of packages make it a great introductory language for student Pi projects.

Q: Where can I find examples of Go programs for the Raspberry Pi?
A: The Go website has articles on Pi projects. GitHub has many Pi Go projects. The blog RasPi.TV also covers Go on the Pi.

Q: Can I use Go for GUI desktop apps on the Pi?
A: Yes, libraries like Ebiten, Fyne and gio provide cross-platform GUI capabilities for creating desktop apps on the Pi.

Q: Is Go a compiled or interpreted language?
A: Go is a compiled language, not interpreted. The go build command compiles Go source to native machine code for the target architecture.

Leave a Comment