Here we collect first user feedbacks with respect to albedos performance.
| Table of Contents |
|---|
With/out Hyperthreading (SMT)
| Model | User | Pro SMT  | Contra SMT  |
|---|---|---|---|
| -- | admin | ~2500 kJ (∑ Esocket[0-7] according to lm_sensors), ~30% less | Nodes need more power (3500 kJ) and get warmer in idle state |
| 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 | ||
Disk Access
| Application | user | node internal NVMe: /tmp | 100 Gb Infinniband GPFS: /albedo | 10 Gb Ethernet /isibhv | |
|---|---|---|---|---|---|
- ...
Runtime compared with ollie
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