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updated readme
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knikolla committed Jan 29, 2025
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Expand Up @@ -27,25 +27,23 @@ The evaluation will include five key benchmarks:
#### (Network) Bandwidth
Big (1MB) sequential I/O requests, 32 concurrently, to stress the network

```
100MB 300GB 600GB
Writes: 193 MiB/s 219 MiB/s 200 MiB/s
Reads: 1990 MiB/s 690 MiB/s 1003 MiB/s
```
| | *NESE* 100MB | *NESE* 300GB | *NESE* 600GB | *Weka* 300GB |
|---------|--------------|--------------|--------------|-------------|
| Writes: | 193 MiB/s | 219 MiB/s | 200 MiB/s | 1060 MiB/s |
| Reads: | 1990 MiB/s | 690 MiB/s | 1003 MiB/s | 1404 MiB/s |


#### Latency
Small (4KB) random I/O requests, no concurrency, to measure good latency

```
[in ms] 100MB 300GB 600GB
Writes Avg.: 37.23 38.7 45.8
Writes Median: 5.1 5.21 7.1
Writes 99%: 371.1 337.6 405.5
Reads Avg.: 0.8 17.62 10.6
Reads Median: 0.39 13.43 10.58
Reads 99%: 10.6 109.57 96.5
```

| in ms | *NESE* 100MB | *NESE* 300GB | *NESE* 600GB | *Weka* 300GB |
|----------------|-------------|--------------|--------------|--------------|
| Writes Avg.: | 37.23 | 38.7 | 45.8 | 0.275 |
| Writes Median: | 5.1 | 5.21 | 7.1 | 0.247 |
| Writes 99%: | 371.1 | 337.6 | 405.5 | 0.525 |
| Reads Avg.: | 0.8 | 17.62 | 10.6 | 0.355 |
| Reads Median: | 0.39 | 13.43 | 10.58 | 0.311 |
| Reads 99%: | 10.6 | 109.57 | 96.5 | 0.545 |


Full results can be found in the [results/](results) folder.
Expand Down Expand Up @@ -75,8 +73,7 @@ The results can be found in the [results/](results) folder.
| A100 | 3500 | NESE Ceph PVC | 10.81 | 165.35 |
| H100 | 3500 | NESE Ceph PVC | 5.58 | 168.05 |
| A100 | 1000 | Local EmptyDir PVC | 24.51 | 729.10 |
| | | Weka PVC | | |
| | | Weka PVC | | |
| A100 | 1000 | Weka PVC | 58.08 | 871.68 |

The below results have not been run on the NERC and are provided purely for reference.

Expand All @@ -86,5 +83,7 @@ The below results have not been run on the NERC and are provided purely for refe

Other results that have been contributed from organizations can be found on the [MLPerf Storage website](https://mlcommons.org/benchmarks/storage/).

## Real Inference Workload
To be derived from Sanjay’s work about the average model to use as an example. Granit (consult Perf group). OPT13B and LLAMA.
## Training Workload
To be performed:
- Resnet with ImageNet dataset
- BERT with [Wikipedia and bookcorpusopen](https://huggingface.co/datasets/sradc/chunked-shuffled-wikipedia20220301en-bookcorpusopen)

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