Top500 Benchmark - HPL Linpack

A common generic benchmark for clusters (or extremly powerful single node workstations) is Linpack, or HPL (High Performance Linpack), which is famous for its use in rankings in the Top500 supercomputer list over the past few decades.

I wanted to see where my various clusters and workstations would rank, historically (you can compare to past lists here), so I built this Ansible playbook which installs all the necessary tooling for HPL to run, connects all the nodes together via SSH, then runs the benchmark and outputs the result.

Why not PTS?

Phoronix Test Suite includes HPL Linpack and HPCC test suites. I may see how they compare in the future.

When I initially started down this journey, the PTS versions didn't play nicely with the Pi, especially when clustered. And the PTS versions don't seem to support clustered usage at all!

Supported OSes

Currently supported OSes:

• Ubuntu (20.04+)
• Raspberry Pi OS (11+)
• Debian (11+)

Other OSes may need a few tweaks to work correctly. You can also run the playbook inside Docker (see the note under 'Benchmarking - Single Node'), but performance will be artificially limited.

Benchmarking - Cluster

Make sure you have Ansible installed (pip3 install ansible), then copy the following files:

• cp example.hosts.ini hosts.ini: This is an inventory of all the hosts in your cluster (or just a single computer).
• cp example.config.yml config.yml: This has some configuration options you may need to override, especially the ssh_* and ram_in_gb options (depending on your cluster layout)

Each host should be reachable via SSH using the username set in ansible_user. Other Ansible options can be set under [cluster:vars] to connect in more exotic clustering scenarios (e.g. via bastion/jump-host).

Tweak other settings inside config.yml as desired (the most important being hpl_root—this is where the compiled MPI, ATLAS, and HPL benchmarking code will live).

Then run the benchmarking playbook inside this directory:

ansible-playbook main.yml

This will run three separate plays:

1. Setup: downloads and compiles all the code required to run HPL. (This play takes a long time—up to many hours on a slower Raspberry Pi!)
2. SSH: configures the nodes to be able to communicate with each other.
3. Benchmark: creates an HPL.dat file and runs the benchmark, outputting the results in your console.

After the entire playbook is complete, you can also log directly into any of the nodes (though I generally do things on node 1), and run the following commands to kick off a benchmarking run:

cd ~/tmp/hpl-2.3/bin/rpi
mpirun -f cluster-hosts ./xhpl

The configuration here was tested on smaller 1, 4, and 6-node clusters with 6-64 GB of RAM. Some settings in the config.yml file that affect the generated HPL.dat file may need diffent tuning for different cluster layouts!

Benchmarking - Single Node

To run locally on a single node, clone or download this repository to the node where you want to run HPL. Make sure the hosts.ini is set up with the default options (with just one node, 127.0.0.1).

All the default configuration from example.config.yml should be copied to a config.yml file, and all the variables should scale dynamically for your node.

Run the following command so the cluster networking portion of the playbook is not run:

ansible-playbook main.yml --tags "setup,benchmark"

For testing, you can start an Ubuntu docker container:

docker run --name top500 -it -v $PWD:/code geerlingguy/docker-ubuntu2204-ansible:latest bash

Then go into the code directory (cd /code) and run the playbook using the command above.

Setting performance CPU frequency

If you get an error like CPU Throttling apparently enabled!, you may need to set the CPU frequency to performance (and disable any throttling or performance scaling).

For different OSes and different CPU types, the way you do this could be different. So far the automated performance setting in the main.yml playbook has only been tested on Raspberry Pi OS. You may need to look up how to disable throttling on your own system. Do that, then run the main.yml playbook again.

Overclocking

Since I originally built this project for a Raspberry Pi cluster, I include a playbook to set an overclock for all the Raspberry Pis in a given cluster.

You can set a clock speed by changing the pi_arm_freq in the overclock-pi.yml playbook, then run it with:

ansible-playbook overclock-pi.yml

Higher clock speeds require more power and thus more cooling, so if you are running a Pi cluster with just heatsinks, you may also require a fan blowing over them if running overclocked.

Results

Here are a few of the results I've acquired in my testing:

Configuration	Result	Wattage	Gflops/W
Turing Pi 2 (4x CM4 @ 1.5 GHz)	44.942 Gflops	24.5W	1.83 Gflops/W
Turing Pi 2 (4x CM4 @ 2.0 GHz)	51.327 Gflops	33W	1.54 Gflops/W
DeskPi Super6c (6x CM4 @ 1.5 GHz)	60.293 Gflops	40W	1.50 Gflops/W
DeskPi Super6c (6x CM4 @ 2.0 GHz)	70.338 Gflops	51W	1.38 Gflops/W
Radxa ROCK 5B (1x RK3588 8-core)	46.669 Gflops	15W	3.11 Gflops/W
Lenovo M710q Tiny (1x i5-7400T @ 2.4 GHz)	72.472 Gflops	41W	1.76 Gflops/W
M2 MacBook Air (1x M2 @ 3.5 GHz, in Docker)	104.68 Gflops	N/A	N/A
M1 Max Mac Studio (1x M1 Max @ 3.2 GHz, in Docker)	264.32 Gflops	66W	4.00 Gflops/W
AMD Ryzen 5 5600x @ 3.7 GHz	229 Gflops	196W	1.16 Gflops/W
Ampere Altra Max M96-28 @ 2.8 GHz	401.88 Gflops	220W	2.01 Gflops/W
Ampere Altra Max M128-30 @ 3.0 GHz	953.47 Gflops	500W	1.91 Gflops/W

You can enter the Gflops in this tool to see how it compares to historical top500 lists.