Building Parallel C++17 SYCL Code on Raspberry Pi 4B+ IoT Boards

Discover how to develop, compile, and run modern parallel C++17 code on Raspberry Pi 4B+ IoT boards using the Khronos CL/SYCL programming model, hipSYCL, and GCC/Clang toolchains.

Our Goals

This guide offers step‑by‑step instructions, practical tips, and best practices for creating efficient parallel applications in C++17/20 with CL/SYCL on Raspberry Pi 4B+ IoT boards, powered by the ARM Cortex‑A72 quad‑core 64‑bit architecture.

Raspberry Pi 4B+ IoT Boards Overview

The latest Raspberry Pi 4B+ boards feature the Broadcom BCM2711B0 SoC, a Quad‑Core ARM Cortex‑A72 @ 1.5 GHz, and up to 8 GiB LPDDR4‑3200 memory. These specifications deliver high performance and scalability for edge‑side parallel computing, enabling real‑time data collection, preprocessing, and efficient off‑loading to data centers.

Key advantages include:

• DSP, SIMD, VFPv4, and hardware virtualization support for accelerated workloads.
• Fast VideoCore‑VI GPU @ 500 MHz and PCI‑Express Gigabit Ethernet for high‑bandwidth connectivity.
• Low‑profile LPDDR4 memory that boosts parallel throughput.

For data mining and heavy parallel workloads, the 4 GiB or 8 GiB configurations are strongly recommended.

Setting Up a Raspberry Pi 4B IoT Board

Start by flashing Raspbian Buster 10.6.0 onto a 16 GB micro‑SD card using Raspberry Pi Imager 1.4. Once flashed, insert the card, connect micro‑HDMI, Ethernet, and a 5 V/3 A USB‑C power supply, then power on the board.

Perform the initial post‑installation steps:

1. Set a root password:

   sudo passwd root

2. Switch to root:

   sudo -s

3. Update system and firmware:

   sudo apt update && sudo apt upgrade -y
   sudo rpi-update

4. Reboot:

   sudo shutdown -r now

5. Install the latest bootloader:

   sudo rpi-eeprom-update -d
   sudo shutdown -r now

6. Run raspi-config and:
   • Update the tool.
   • Disable the desktop GUI (Boot → Console Autologin).
   • Expand the root partition.

After the final reboot, log in as root. Install networking tools and OpenSSH to enable remote access:

sudo apt install -y net-tools openssh-server

Configure a static IP for eth0 in /etc/network/interfaces and enable root SSH login in /etc/ssh/sshd_config (uncomment the relevant lines). Restart the SSH service and connect using an SSH client such as MobaXterm.

Developing Parallel C++17 Code with CL/SYCL

The Khronos Group introduced CL/SYCL as a modern abstraction over OpenCL, enabling heterogeneous parallelism across CPUs, GPUs, and FPGAs. Below is a minimal SYCL kernel that runs on the host CPU:

#include <CL/sycl.hpp>
using namespace cl::sycl;
constexpr std::uint32_t N = 1000;
int main(){
    queue q{};
    q.submit([&](handler &cgh){
        cgh.parallel_for<class Kernel>(
            range<1>{N},
            [=](id<1> idx){
                // Parallel work here
            });
    });
    q.wait();
}

To target the Raspberry Pi’s ARM AArch64 architecture, you need an SYCL implementation that supports it. The triSYCL project offers experimental support for cross‑platform builds, while the more mature hipSYCL library provides stable support for ARM. Install the appropriate toolchains:

• GCC/G++ 10.x.x cross‑compiler for ARM.
• LLVM/Clang 9.x.x native toolchain for AArch64.

Build your SYCL application with hipSYCL, linking against the ARM backend, and deploy the executable directly to the Raspberry Pi. Running the binary on the Pi will execute the kernel on the CPU or any available accelerator, delivering measurable speed‑ups for data‑intensive workloads.

By following this workflow, developers can harness the full potential of Raspberry Pi 4B+ IoT boards for edge‑side parallel computing, benefiting from the growing ecosystem of SYCL libraries and open‑source toolchains.