How to Benchmark ROCm Performance Gains on AMD Radeon AI PRO R9700

Introduction

Curious about the performance leap between ROCm 7.0.0 and the latest ROCm 7.2.3 on your AMD Radeon AI PRO R9700? This guide walks you through a systematic benchmark comparison, using a workstation like the System76 Thelio Major as a reference. By following these steps, you'll quantify the impact of updating user-space ROCm components from late summer to the current stable release. Whether you're a developer, researcher, or AI enthusiast, this hands-on test reveals tangible gains in workloads like machine learning and HPC.

What You Need

• AMD Radeon AI PRO R9700 (RDNA4 workstation GPU) installed in a compatible system (e.g., System76 Thelio Major or similar PC with PCIe 4.0/5.0 and adequate power supply).
• Linux distribution (Ubuntu 22.04 LTS recommended; ROCm supports Ubuntu, RHEL, or SLES).
• ROCm installation packages for versions 7.0.0 and 7.2.3 (download from AMD's ROCm repository or use the amdgpu-install script).
• Benchmarking tools such as rocBLAS, rocFFT, MIOpen, or TensorFlow/PyTorch with ROCm backends. For consistency, use the AI/Benchmark suite from AMD or open-source equivalents.
• Terminal access with sudo privileges and basic Linux command-line familiarity.
• Storage space – at least 20 GB for ROCm packages and benchmark artifacts.

Step-by-Step Guide

Step 1: Prepare Your System and Baseline Drivers

Ensure your system is clean and up to date. Update the kernel and pre-install any required dependencies:

sudo apt update && sudo apt upgrade -y
sudo apt install build-essential dkms linux-headers-$(uname -r) -y

Verify that the Radeon AI PRO R9700 is detected with lspci | grep -i amd. Install the ROCm kernel driver (if not already present) using the amdgpu-install script from AMD's website. For this guide, we start from a clean slate with ROCm 7.0.0.

Step 2: Install ROCm 7.0.0

Download and install the 7.0.0 package. If you're using the official AMD repository:

wget https://repo.radeon.com/amdgpu-install/7.0.0/ubuntu/jammy/amdgpu-install_6.0.60001-1_all.deb
sudo dpkg -i amdgpu-install_6.0.60001-1_all.deb
sudo amdgpu-install --usecase=rocm,hip,rocmdev

After installation, reboot and verify ROCm 7.0.0 is active with rocminfo | grep -i version and /opt/rocm/bin/rocminfo. Ensure the GPU is listed.

Step 3: Run Reference Benchmarks

Choose a consistent benchmark suite. For example, use AMD's ROCm Benchmark Suite (available on GitHub) or run standard tests with rocBLAS gemm and rocFFT. Execute the following commands within each benchmark directory:

cd /opt/rocm/bin
./rocblas-bench --n 1024 --k 1024 --m 1024 --alpha 1 --beta 0 --a_type f32 --b_type f32 --c_type f32 --compute_type f32
./rocfft-bench --size 4096 --type c2c --precision double

Record outputs (latency, GFLOPS, bandwidth) in a file named roc70_results.txt. Repeat each test 3-5 times to get a stable average.

Step 4: Upgrade ROCm to Version 7.2.3

Remove the old ROCm packages first:

sudo amdgpu-install --uninstall --rocmrelease=7.0.0
sudo apt autoremove -y

Then download and install ROCm 7.2.3:

wget https://repo.radeon.com/amdgpu-install/7.2.3/ubuntu/jammy/amdgpu-install_7.2.3.60001-1_all.deb
sudo dpkg -i amdgpu-install_7.2.3.60001-1_all.deb
sudo amdgpu-install --usecase=rocm,hip,rocmdev

Reboot, then verify the new version with /opt/rocm/bin/rocminfo and check that the GPU is recognized.

Step 5: Repeat Benchmarks with ROCm 7.2.3

Exactly repeat the same benchmark commands from Step 3, using the same input parameters and tools. Save results to roc723_results.txt. Run the same number of iterations to ensure fairness.

Step 6: Compare and Analyze the Results

Create a simple side-by-side comparison. For example, with command-line tools:

diff -u roc70_results.txt roc723_results.txt

Or use a spreadsheet. Look for changes in:

• Throughput (GFLOPS, memory bandwidth).
• Latency (milliseconds per operation).
• Any errors or improvements in kernel launches.

Calculate percentage differences. A typical outcome might show a 5-15% improvement in matrix operations and FFT workloads due to ROCm 7.2.3's optimizations.

Tips for Accurate and Meaningful Comparisons

• Isolate variables: Avoid running any other heavy processes during benchmarks. Close GUI sessions and disable background services like update managers.
• Use the same kernel and drivers: If possible, keep the same Linux kernel (e.g., 6.5.x) across both ROCm versions to avoid cpu-level differences affecting GPU results.
• Temperature control: Ensure the GPU temperature stays under 80°C to prevent throttling. Use a cooling fan profile or ambient conditioning.
• Multiple runs: Always run each benchmark at least three times and record the median value to filter out run-to-run variance.
• Document system state: Note the exact ROCm build, driver version, GPU firmware, and any environment variables (HCC_AMDGPU_TARGET, ROCM_PATH) that might affect performance.
• Use pre-sourced packages: For reproduceability, using AMD's official repository rather than building from source.
• Real-world workloads: Consider also testing with frameworks like PyTorch (with torch.backends.optuna.enable_cuda=True for ROCm) or TensorFlow to see if gains translate to full models.

By following this guide, you'll have a clear picture of how upgrading from ROCm 7.0.0 to 7.2.3 boosts your Radeon AI PRO R9700's performance. Happy benchmarking!