Update README.md

## Figure of Merit

The primary figure of merit for the ML4NSE (defined in detail in https://doi.org/10.1615/JMachLearnModelComput.2023048607) workflow benchmark is computational throughput (i.e., the inverse of time-to-solution). Some useful secondary figures of merit will be:

The primary figure of merit for the ML4NSE (defined in detail in https://doi.org/10.1615/JMachLearnModelComput.2023048607) workflow benchmark is the number of voxels per second:

$$(#voxels * #replicas) / (workflow_makespan)$$

Some useful secondary figures of merit will be:

- Exchange bandwidth (ingress) at the gateway node which multiplexes among input streams

An additional potential figure of merit that could demonstrate the robustness of the system would include any active guidance between the Compute and Services Clusters; the latency involved in control operations becomes crucial. Specifically, it's essential to assess the duration a compute job is held while disseminating new control information. Also, as additional input streams and output consumers are added, the effect on end-to-end time-to-solution could be affected.

| Dataset | Dimension | # Nodes | # Replicas | Sending Transfer Rate | Avg. Receiving Transfer Rate (per rank) | Throughput |

| Dataset | Dimension | #Nodes | #Replicas | Send. Transfer Rate | Avg. Rec. Transfer Rate (per rank) | FOM (#voxels*#replicas/second) |

| ------ | ------ | ------ | ------ | ------ | ------ | ------ | 

| p_322_data_np_res_16.npy | 16 x 16 x 16 | 900 | 100 | 62.79 Gbps | 10.43 Mbps | 1.144 × 10<sup>-3</sup> |

| p_322_data_np_res_16.npy | 8 x 8 x 8 | 90 | 10 | 62.79 Gbps | 10.43 Mbps | 1.144 × 10<sup>-3</sup> |

| p_322_data_np_res_16.npy | 8 x 8 x 8 | 900 | 100 | 62.79 Gbps | 10.43 Mbps | 1.144 × 10<sup>-3</sup> |