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

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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!