SUMO HPC fat node for GAP training

We are very excited to welcome our new dedicated state-of-the-art “fat node” (jargon for “high memory server”) SUMO, which we will use primarily to train machine learning GAP potentials. These potentials use lots of structural and energy/forces atomic data, and thus require large amounts of RAM. Our SUMO-I machine (we’re being optimistic that there will be SUMO-II, SUMO-III, …, in the future) boasts an impressive 3TB of RAM and 48 CPU cores.

This server will complement the amazing CSC resources that we have been using so far from the Puhti supercluster, which has a “hugemem” partition with 12 nodes equipped with 768 GB of RAM and another 12 nodes equipped with 1.5 TB of RAM. This means that our new SUMO server will allow us to use twice more training data, plus reduce queue times since it’s a machine fully dedicated to our group. For optimal performance, we will combine Puhti resources with SUMO, where final fits incorporating as much data as we can fit within 3 TB of RAM will be run on SUMO, and both systems will provide us with plenty of CPU power and flexibility during the database and potential development stages.

Today, Ivan Degtyarenko, who is IT Specialist at Aalto University, gave Jan Kloppenburg and myself a tour of the facilities where SUMO-I will reside physically. SUMO-I will be housed at the CSC headquarters in Keilaniemi (next door from the Aalto Otaniemi campus) and integrated into the Triton cluster. It will be managed by the Science-IT project at Aalto University, which means we will have access to their existing HPC infrastructure (fast network, scratch filesystem, expert maintenance and support, etc.).

At the Life Science Center in Keilaniemi (where the CSC headquarters are)

The acquisition of SUMO has been made possible thanks to the financial support from the Academy of Finland, and will give our group an edge for the development and deployment of accurate and fast machine learning interatomic potentials. We’re looking forward to start burning CPU time!

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