# core.solver.DataModel { #mlsysim.core.solver.DataModel }

```python
core.solver.DataModel()
```

Analyzes the 'Data Wall' — the throughput bottleneck between storage and compute.

This solver models the data pipeline constraints, comparing the data demand
of a workload (e.g., training tokens or high-resolution video frames)
against the physical bandwidth of the storage hierarchy and IO interconnects.

Literature Source:
1. Janapa Reddi et al. (2025), "Machine Learning Systems," Chapter 4 (Data Engineering).
2. Beitzel et al. (2024), "The Data Wall: Scaling Laws for Data Ingestion in AI."
3. Mohan et al. (2022), "Analyzing and Mitigating Data Bottlenecks in Deep Learning Training."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.core.solver.DataModel.solve) | Solves for data pipeline feasibility. |

### solve { #mlsysim.core.solver.DataModel.solve }

```python
core.solver.DataModel.solve(workload_data_rate, hardware)
```

Solves for data pipeline feasibility.

#### Parameters {.doc-section .doc-section-parameters}

| Name               | Type         | Description                                               | Default    |
|--------------------|--------------|-----------------------------------------------------------|------------|
| workload_data_rate | Quantity     | The required data ingestion rate (e.g., TB/hour or GB/s). | _required_ |
| hardware           | HardwareNode | The hardware node with storage and interconnect specs.    | _required_ |

#### Returns {.doc-section .doc-section-returns}

| Name   | Type             | Description                                                   |
|--------|------------------|---------------------------------------------------------------|
|        | Dict\[str, Any\] | Pipeline metrics including utilization and stall probability. |