Hardware Overview
The Explorer cluster provides access to over 45,000 CPU cores and over 525 GPUs to all Northeastern faculty and students free of charge. Hardware currently available for research consists of a combination of Intel Xeon (Sapphire Rapids, Ice Lake, Cascadelake, Skylake, Haswell, Sandybridge, and Ivybridge) and AMD (Zen, Zen2, Zen3, and Zen4) CPU microarchitectures. Additionally, a selection of NVIDIA Pascal (P100), Volta (V100), Turing (T4), Ampere (A100), RTX (A5000 and A6000), Lovelace (L40), and Hopper (H200 and H100) GPUs.
Explorer is connected to the university network over 10 Gbps Ethernet (GbE) for high-speed data transfer, and Explorer provides 6 PB of available storage on a high-performance file system. Compute nodes are connected with either 10 GbE or high data rate InfiniBand (200 Gbps or 100 Gbps), supporting all types and scales of computational workloads.
If you would like to purchase hardware, please schedule a Consultation with the RC team first.
CPU Nodes
The table below shows the feature names, number of nodes by partition type (RC – Owned and PI – Owned). The feature name follows archspec microarchitecture specification.
| CPU Type | RC – Owned Nodes | PI – Owned Nodes |
|---|---|---|
| Sandy Bridge | 2 | 0 |
| Sandy BridgeEP | 24 | 1 |
| Ivy BridgeEP | 26 | 57 |
| Haswell | 74 | 11 |
| Skylake | 19 | 62 |
| Zen | 4 | 0 |
| Zen2 | 18 | 126 |
| Zen3 | 4 | 29 |
| Cascade Lake | 88 | 36 |
| Ice Lake | 0 | 9 |
| Sapphire Rapids | 0 | 52 |
| Emerald Rapids | 0 | 10 |
GPU Nodes
The table below shows the GPU types, architecture, memory and other features of the GPUs on the HPC cluster. For more information about GPUs, see Working with GPUs. If you are interested in more information about the different partitions on the cluster, including the number of nodes per partition, running time limits, job submission limits, and RAM limits, see Partitions.
Once you know what GPU type you want to use, you can learn to specify the GPU type you would like to use.
| GPU Type | RC – Owned Nodes (x# GPUs) | PI – Owned Nodes (x# GPUs) |
|---|---|---|
| V100-PCLe | 4 (x4) 32 GB | 1 (x2) 16 GB 1 (x4) 16 GB |
| V100-SXM2 | 16 (x4) 32 GB 1 (x3) 32 GB | 8 (x4) 16 GB 12 (x4) 16 GB |
| T4 | 2 (x4) | 1 (x4) |
| A100 | 1 (x2) 40 GB 2 (x4) 80 GB | 3 (x2) 40 GB 6 (x8) 40 GB 1 (x8) 80 GB 1 (x4) 80 GB 1 (x3) 80 GB |
| K20m | 0 | 4 (x1) |
| K80 | 0 | 1 (x2) |
| P100 | 0 | 1 (x3) 12 GB 3 (x4) 12 GB |
| Quadro RTX 8000 | 0 | 3 (x3) |
| Vega 20 | 0 | 13 (x8) |
| A6000 | 0 | 4 (x8) |
| A30 | 0 | 1 (x3) |
| A5000 | 0 | 5 (x8) |
| H100 | 0 | 1 (x4) 80 GB |
| H200 | 4 (x8) 144 GB | 0 |
| L40S | 0 | 2 (x8) |
How Can Research Computing Support You?
Accelerate your research at any stage by leveraging our online user guides, hands-on training sessions, and one-on-one guidance.
