Updated README with MLperf description and results

README.md

@@ -12,5 +12,38 @@ on the MOC and NERC, specifically related to AI applications.
 
 ## MLPerf Storage / `dlio_benchmark`
 
+MLPerf Storage benchmarks the performance for training workloads.
+This is achieved by generating a dataset and simulating the process of training over the generated dataset.
+It does not make use of GPUs, and the time which the GPU would have spent on training over each sample of the dataset has been replaced with a sleep command.
+Processing time on actual hardware (A100 and H100) has been measured in order to calculate the correct sleep time amount for each sample.
+Training is run over 5 epochs and does not perform checkpointing.
 
-## End to End
+The main metric for the MLPerf Storage experiment is `Accelerator Utilization`.
+This is measured as the fraction of time that the GPU would spend processing compared to the overall duration of the experiment, as defined by the formula `AU = Accelerator Total Time / Total Duration = Accelerator Total Time / (Accelerator Total Time + Storage Load Time)`.
+
+By default, MLPerf Storage defines an accelerator utilization score below 90% as a fail.
+
+In our setup of the experiment the dataset is loaded from a Persistent Volume Claim that is hosted on the NESE ceph cluster.
+The training workload is unet3d, 1 simulated GPU, and 1000 (~140GB) and 3500 (~500GB) samples of dataset.
+Each sample ranges in size from around 80MB to 200MB.
+Kubernetes job and PVC definition can be found in the [k8s/](k8s) folder.
+
+The results can be found in the [results/](results) folder.
+
+| Simulated GPU | Samples | Storage Type       | AU (%) | MB/s   |
+|---------------|---------|--------------------|--------|--------|
+| 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           |        |        |
+
+The below results have not been run on the NERC and are provided purely for reference.
+
+| Simulated GPU | Samples | Storage Type         | AU (%) | MB/s    |
+|---------------|---------|----------------------|--------|---------|
+| A100          | 1200    | Macbook Pro 14" NVMe | 99.16  | 1495.76 |
+
+Other results that have been contributed from organizations can be found on the [MLPerf Storage website](https://mlcommons.org/benchmarks/storage/). 
+
+## Actual Inference Workload