Hardware Information¶
The following hardware summaries may be useful for selecting partitions for workflows and for grant proposal writing. If any information is missing that would be helpful to you, please be sure to contact us or create an issue on our tracker.
Tip
The tables in this section are wide and can be scrolled horizontally to display more information.
Cheaha HPC Cluster¶
Summary¶
The table below contains a summary of the computational resources available on Cheaha and relevant Quality of Service (QoS) Limits. QoS limits allow us to balance usage and ensure fairness for all researchers using the cluster. QoS limits are not a guarantee of resource availability.
In the table, Slurm partitions are grouped by shared QoS limits on cores, memory, and GPUs. Node limits are applied to partitions independently. All limits are applied to researchers independently.
Examples of how to make use of the table:
- Suppose you submit 30 jobs to the "express" partition, and suppose each job needs 10 cores each. Hypothetically, in order for all of the jobs to start at once, 300 cores would be required. The QoS limit on cores is 264 on the "express" partition, so at most 26 jobs (260 cores) can start at once. The remaining 4 jobs will be held in queue, because starting one more would go beyond the QoS limit (270 > 264).
- Suppose you submit 5 jobs to the "medium" partition and 5 to the "long" partition, each requiring 1 node. Then, 10 total nodes would be needed. In this case, it is possible that all 10 jobs can start at once because partition node limits are separate. If all 5 jobs start, jobs on the "medium" partition.
- Suppose you submit 5 jobs to the "amperenodes" partition and 5 to "amperenodes-medium", for a total of 10 A100 GPUs. Additionally, you also submit 4 jobs to the "pascalnodes" partition totaling 8 P100 GPUs. Then 4 of the "gpu: ampere" group jobs can start at once, because the QoS limit is 4 GPUs there. Additionally, all 4 of the "gpu: pascal" group jobs, because the QoS limit is 8 GPUs there. In this case, the QoS for each group is separate.
| Partition | Time Limit in Hours | Nodes (Limit/Partition) | Cores/Node (Limit/Person) | Mem GB/Node (Limit/Person) | GPU/Node (Limit/Person) |
|---|---|---|---|---|---|
| cpu: amd | |||||
| amd-hdr100 | 150 | 34 (5) | 128 (264) | 504 (3072) | |
| cpu: intel | |||||
| express | 2 | 51 (~) | 48 (264) | 754 (3072) | |
| short | 12 | 51 (44) | 48 (264) | 754 (3072) | |
| medium | 50 | 51 (44) | 48 (264) | 754 (3072) | |
| long | 150 | 51 (5) | 48 (264) | 754 (3072) | |
| gpu: ampere | |||||
| amperenodes | 12 | 20 (TBD) | 32 (64) | 189 (384) | 2 (4) |
| amperenodes-medium | 48 | 20 (TBD) | 32 (64) | 189 (384) | 2 (4) |
| gpu: pascal | |||||
| pascalnodes | 12 | 18 (~) | 28 (56) | 252 (500) | 4 (8) |
| pascalnodes-medium | 48 | 7 (~) | 28 (56) | 252 (500) | 4 (8) |
| mem: large | |||||
| largemem | 50 | 13 (10) | 24 (290) | 755 (7168) | |
| largemem-long | 150 | 5 (10) | 24 (290) | 755 (7168) |
The full table can be downloaded here.
Details¶
Detailed hardware information, including processor and GPU makes and models, core clock frequencies, and other information for current hardware are in the table below.
| Generation | Compute Type | Total Cores | Total Memory Gb | Total Gpus | Cores Per Node | Cores Per Die | Dies Per Node | Die Brand | Die Name | Die Frequency Ghz | Memory Per Node Gb | Gpu Per Node | Gpu Brand | Gpu Name | Gpu Memory Gb | Nodes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | cpu: amd | 128 | 1024 | 2 | 1 | 2 | AMD | Opteron 242 | 1.6 | 16 | 64 | |||||
| 2 | cpu: intel | 192 | 1152 | 8 | 4 | 2 | Intel | Xeon E5450 | 3 | 48 | 24 | |||||
| 3 | cpu: intel | 384 | 1536 | 12 | 6 | 2 | Intel | Xeon X5650 | 2.66 | 48 | 32 | |||||
| 3 | cpu: intel | 192 | 1536 | 12 | 6 | 2 | Intel | Xeon X5650 | 2.66 | 96 | 16 | |||||
| 4 | cpu: intel | 48 | 1152 | 16 | 8 | 2 | Intel | Xeon X5650 | 2.7 | 384 | 3 | |||||
| 5 | cpu: intel | 192 | 1152 | 16 | 8 | 2 | Intel | Xeon E2650 | 2 | 96 | 12 | |||||
| 6 | cpu: intel | 336 | 5376 | 24 | 12 | 2 | Intel | Xeon E5-2680 v3 | 2.5 | 384 | 14 | |||||
| 6 | cpu: intel | 912 | 9728 | 24 | 12 | 2 | Intel | Xeon E5-2680 v3 | 2.5 | 256 | 38 | |||||
| 6 | cpu: intel | 1056 | 5632 | 24 | 12 | 2 | Intel | Xeon E5-2680 v3 | 2.5 | 128 | 44 | |||||
| 7 | gpu: pascal | 504 | 4608 | 72 | 28 | 14 | 2 | Intel | Xeon E5-2680 v4 | 2.4 | 256 | 4 | NVIDIA | Tesla P100 | 16 | 18 |
| 8 | cpu: intel | 504 | 4032 | 24 | 12 | 2 | Intel | Xeon E5-2680 v4 | 2.5 | 192 | 21 | |||||
| 8 | mem: large | 240 | 7680 | 24 | 12 | 2 | Intel | Xeon E5-2680 v4 | 2.5 | 768 | 10 | |||||
| 8 | mem: large | 96 | 6144 | 24 | 12 | 2 | Intel | Xeon E5-2680 v4 | 2.5 | 1536 | 4 | |||||
| 9 | cpu: intel | 2496 | 39936 | 48 | 24 | 2 | Intel | Xeon Gold 6248R | 3 | 768 | 52 | |||||
| 10 | cpu: amd | 4352 | 17408 | 128 | 64 | 2 | AMD | Epyc 7713 Milan | 2 | 512 | 34 | |||||
| 11 | gpu: ampere | 2560 | 10240 | 40 | 128 | 64 | 2 | AMD | Epyc 7763 Milan | 2.45 | 512 | 2 | NVIDIA | A100 | 80 | 20 |
| 1 | cpu: intel | 240 | 960 | 48 | 12 | 4 | Intel | Xeon Gold 6248R | 3 | 192 | 5 | |||||
| 1 | gpu: ampere | 512 | 4096 | 32 | 128 | 64 | 2 | AMD | Epyc 7742 Rome | 2.25 | 1024 | 8 | NVIDIA | A100 | 40 | 4 |
| 1 | cpu: intel | 144 | 576 | 48 | 12 | 4 | Intel | Xeon Gold 6248R | 3 | 192 | 3 | |||||
| 1 | gpu: ampere | 512 | 4096 | 32 | 128 | 64 | 2 | AMD | Epyc 7742 Rome | 2.25 | 1024 | 8 | NVIDIA | A100 | 40 | 4 |
The full table can be downloaded here.
The table below is a theoretical analysis of FLOPS (floating point operations per second) based on processor instructions and core counts, and is not a reflection of efficiency in practice.
| Generation | Cpu Tflops Per Node | Gpu Tflops Per Node | Tflops Per Node | Nodes | Tflops |
|---|---|---|---|---|---|
| 7 | 1.08 | 17.06 | 18.14 | 18 | 326.43 |
| 8 | 0.96 | 0.96 | 21 | 20.16 | |
| 8 | 0.96 | 0.96 | 10 | 9.60 | |
| 8 | 0.96 | 0.96 | 4 | 3.84 | |
| 9 | 2.30 | 2.30 | 52 | 119.81 | |
| 10 | 4.10 | 4.10 | 34 | 139.26 | |
| 11 | 5.02 | 15.14 | 20.15 | 20 | 403.10 |
| Total | 1,022.20 |
The full table can be downloaded here.
For information on using Cheaha, see our dedicated section.
Cloud Service at cloud.rc¶
The Cloud service hardware consists of 5 Intel nodes and 4 DGX-A100 nodes. A description of the available hardware are summarized in the following table.
| Fabric | Generation | Compute Type | Partition | Total Cores | Total Memory Gb | Total Gpus | Cores Per Node | Memory Per Node Gb | Nodes | Cpu Info | Gpu Info |
|---|---|---|---|---|---|---|---|---|---|---|---|
| cloud | 1 | cpu | 240 | 960 | 48 | 192 | 5 | Intel Xeon Gold 6248R 3.00 GHz | |||
| cloud | 1 | gpu | 512 | 4096 | 32 | 128 | 1024 | 4 | AMD Epyc 7742 Rome 2.25 GHz | NVIDIA A100 40 GB | |
| Total | 752 | 5056 | 32 | 9 |
The full table can be downloaded here.
The table below is a theoretical analysis of FLOPS (floating point operations per second) based on processor instructions and core counts, and is not a reflection of efficiency in practice.
| Generation | Cpu Tflops Per Node | Gpu Tflops Per Node | Tflops Per Node | Nodes | Tflops |
|---|---|---|---|---|---|
| 1 | 2.30 | 2.30 | 5 | 11.52 | |
| 1 | 4.61 | 77.97 | 82.58 | 4 | 330.3 |
| Total | 341.82 |
The full table can be downloaded here.
For information on using our Cloud service at cloud.rc, see our dedicated section.
Kubernetes Container Service¶
Important
The Kubernetes fabric is still in deployment and not ready for researcher use. We will be sure to inform you when the service is ready. The following information is planned hardware.
The Kubernetes container service hardware consists of 5 Intel nodes and 4 DGX-A100 nodes. A description of the available hardware are summarized in the following table.
| Fabric | Generation | Compute Type | Partition | Total Cores | Total Memory Gb | Total Gpus | Cores Per Node | Memory Per Node Gb | Nodes | Cpu Info | Gpu Info |
|---|---|---|---|---|---|---|---|---|---|---|---|
| container | 1 | cpu | 144 | 576 | 48 | 192 | 3 | Intel Xeon Gold 6248R 3.00 GHz | |||
| container | 1 | gpu | 512 | 4096 | 32 | 128 | 1024 | 4 | AMD Epyc 7742 Rome 2.25 GHz | NVIDIA A100 40 GB | |
| Total | 656 | 4672 | 32 | 7 |
The full table can be downloaded here.
The table below is a theoretical analysis of FLOPS (floating point operations per second) based on processor instructions and core counts, and is not a reflection of efficiency in practice.
| Generation | Cpu Tflops Per Node | Gpu Tflops Per Node | Tflops Per Node | Nodes | Tflops |
|---|---|---|---|---|---|
| 1 | 2.30 | 2.30 | 3 | 6.91 | |
| 1 | 4.61 | 77.97 | 82.58 | 4 | 330.3 |
| Total | 337.21 |
The full table can be downloaded here.