The Advanced Research Computing center’s HPC Cluster (ARC) is UC’s large-memory computer cluster. Designed to support data-intensive computing, ARC is particularly well-suited for running software and applications that require large amounts of computer memory and processing. ARC provides specialized deep GPU partitions for researchers with deep learning applications and other applications that require GPUs. ARC currently has 2 major clusters, namely ARC1 and ARC2. The hardware specifications for each are summarized in the table below
ARCC1 (pilot cluster)
| Number of nodes | Processors per node | Cores per nodes | Memory per node | Omnipath | Accelerator card | Speed |
| 36 | 2 Intel Xeon Gold 6148 CPUs | 40 | 192 GB | 100 GB/s | N/A | 2.4-3.7 GHz |
| 1 | 2 Intel Xeon Gold 6148 CPUs | 40 | 192 GB | 100 GB/s | 2 NVIDIA Tesla V100-32GB GPU | 2.4-3.7 GHz |
ARCC2
| Node Type | Number of nodes | Processors per node | Cores per nodes | Memory per node | InfiniBand | Accelerator card | Speed |
| RM | 95 | 2 AMD EPYC 7452 CPUS | 64 | 256GB RAM | 100 GB/s | N/A | 2.35-3.35 GHz |
| GPU | 10 | 2 AMD EPYC 7452 CPUS | 64 | 1024GB RAM | 100 GB/s | 2 NVIDIA Tesla A100-40GB GPU | 2.35-3.35 GHz |
| Large Mem | 1 | 2 AMD EPYC 7452 CPUS | 64 | 2048GB RAM | 100 GB/s | N/A | 2.35-3.35 GHz |
UC’s newest High-Performance Computing, AI and High-Performance Data Analytics (HPDA) system
UC’s newest high-performance computing and data analytics system, available Fall 2022, is funded in part by investments from the Office of Research, IT@UC, colleges and departments and a significant grant from the National Science Foundation’s Major Research Instrumentation (MRI) program (award #2018617). UC is partnered with Hewlett Packard Enterprises (HPE) to architect a purpose-built compute resource for demanding High-Performance Computing (HPC) and Artificial Intelligence (AI) applications.
ARCC2 provides transformative capability for rapidly evolving, computation-intensive and data-intensive research, supporting both traditional and non-traditional research communities and applications. The converged, scalable HPC, machine learning and data tools create an opportunity for collaboration and converged research, prioritizing researcher productivity and ease of use with an easy-to-use web-based interface.
Innovation
- AMD EPYC 7452 CPUs: 64-core 2.35–3.35 GHz
- AI scaling to 20 Tesla A100-40GB GPUs
- Mellanox HDR-100 InfiniBand supports in-network MPI-Direct, RDMA, GPUDirect, SR-IOV, and data encryption
- Cray ClusterStor E1000 Storage System
- Open OnDemand – Web based interface
Regular Memory
Regular Memory (RM) CPU nodes provide extremely powerful general-purpose computing, machine learning and data analytics, AI inferencing, and pre- and post-processing. 95 RM nodes will have:
- Two AMD EPYC CPUS, each with:
- 32 cores
- 2.35-3.35GHz
- 128MB L3
- 256GB of RAM
- 8 memory channels
- SATA SSD (960GB)
- Mellanox ConnectX-6 HDR InfiniBand 100Gb/s Adapter
Large Memory
Large Memory (LM) node will provide 1TB of shared memory for genome sequence assembly, graph analytics, statistics, and other applications requiring a large amount of memory for which distributed-memory implementations are not available.
ARCC2’s 1 LM nodes will consist of:
- Two AMD EPYC CPUS, each with:
- 32 cores
- 2.35-3.35GHz
- 128MB L3
- 8 memory channels
- 1024GB of RAM
- Mellanox ConnectX-6 HDR InfiniBand 100Gb/s Adapter
GPU
10 GPU nodes provide exceptional performance and scalability for deep learning and accelerated computing. Each GPU node will contain:
- Two NVIDIA Tesla A100 40GB GPUs
- Two AMD EPYC CPUS, each with:
- 32 cores
- 2.35-3.35GHz
- 128MB L3
- 8 memory channels
- 1024GB of RAM
- SATA SSD (960GB)
- Mellanox ConnectX-6 HDR InfiniBand 100Gb/s Adapter
Please contact Jane Combs or arc_info@uc.edu with questions