Here we collect first user feedbacks with respect to albedos performance.
With/out Hyperthreading (SMT)
| Model | User | Pro SMT  | Contra SMT  |
|---|---|---|---|
| idle | admin | - | (∑ Esocket[0-7] according to lm_sensors: |
| stress-ng stream | admin | -- | ~13% slower |
| FESOM | NEC | Using 128 Threads per node: 3% faster (probably because the (buggy) GXFS daemon can use a virtual core) | Using 256 Thread per node: 10% slower |
| Python AI | vhelm | no impact/difference | no impact/difference |
GPU nodes (A40 vs. A100)
| Model | User | A40 vs. A100 | |
|---|---|---|---|
| tensorflow-gpu AI application | vhelm | no difference | |
python3, matrix operations with with numpy (fat) vs cupy (gpu) | sviquera |
| |
Disk Access
| Application | user | node internal NVMe: /tmp | 100 Gb Infinniband GPFS: /albedo | 10 Gb Ethernet /isibhv | |
|---|---|---|---|---|---|
- ...
Runtime compared with ollie
- ...