Cluster Speeds Molecular Dynamics Research with NVIDIA GPUs

The University of Richmond is a private liberal arts university, founded in 1830, that operates five schools offering undergraduate, master’s and law degrees.  

The Objective

Dr. Carol Parish, Professor of Chemistry, leads a research group that focuses on understanding the dynamical behavior of interesting molecular systems. The Parish group uses quantum mechanics, conformational searching and free energy simulation to answer questions about the structure, energy and dynamics of HIV-1 protease inhibitor drugs, homology modeling of membrane-bound desaturase enzymes and Bergman cyclization in enediyne anti-cancer warhead drugs.

The lab needed a high performance computing solution that included graphics processing units or GPUs.

“I’ve had to run my jobs without GPUs, and we knew using the cluster would make a huge difference,” said Erica Modeste, HHMI Post Baccalaureate Research Assistant. “A lot of times with CPUs, our calculations will die halfway through.”

The Solution

The Parish team needed a high performance cluster to support the large number of students working on a diverse range of research projects. Advanced Clustering provided a 22-node cluster including NVIDIA GPUs.

The Results

The team runs molecular dynamics and quantum mechanics calculations on the cluster, which has multiple GPUs.

“Usually if you are running quantum calculations, your jobs require more memory,” said Modeste. “One of the pros of having GPUs is that we can run our jobs 10 times faster than on CPUs. A lot of communication needs to happen between nodes. The cluster Advanced Clustering Technologies built for us has doubled the speed of our timestamp. We could get 15 nanoseconds per day with CPUs. We get 25 nanoseconds per day with GPU, which is a big deal in computer world.”

Optimizing the cluster for varied workloads often requires technical support.

“The team at Advanced Clustering is very responsive to your needs. They respond really quickly. We usually hear back within the hour, which is rare,” Modeste said.

The team is keeping the cluster busy, with almost 100% utilization.

“Everyone prefers to run our calculations on the cluster,” Modeste said. “It’s the fastest one we have right now. It’s easier to keep all of the information there. We’re always on a time crunch.”