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2bbe3e1a69
Replace 9 old tutorials with 12 new numbered tutorials (00-11) covering roofline through full-stack audit. Redesign landing page, add models-and-solvers and extending-the-engine guides. Add __main__.py, cli.py, and cli/ package for command-line interface.
44 lines
1.8 KiB
Plaintext
44 lines
1.8 KiB
Plaintext
# core.solver.DataModel { #mlsysim.core.solver.DataModel }
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```python
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core.solver.DataModel()
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```
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Analyzes the 'Data Wall' — the throughput bottleneck between storage and compute.
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This solver models the data pipeline constraints, comparing the data demand
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of a workload (e.g., training tokens or high-resolution video frames)
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against the physical bandwidth of the storage hierarchy and IO interconnects.
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Literature Source:
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1. Janapa Reddi et al. (2025), "Machine Learning Systems," Chapter 4 (Data Engineering).
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2. Beitzel et al. (2024), "The Data Wall: Scaling Laws for Data Ingestion in AI."
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3. Mohan et al. (2022), "Analyzing and Mitigating Data Bottlenecks in Deep Learning Training."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.core.solver.DataModel.solve) | Solves for data pipeline feasibility. |
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### solve { #mlsysim.core.solver.DataModel.solve }
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```python
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core.solver.DataModel.solve(workload_data_rate, hardware)
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```
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Solves for data pipeline feasibility.
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#### Parameters {.doc-section .doc-section-parameters}
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| Name | Type | Description | Default |
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|--------------------|--------------|-----------------------------------------------------------|------------|
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| workload_data_rate | Quantity | The required data ingestion rate (e.g., TB/hour or GB/s). | _required_ |
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| hardware | HardwareNode | The hardware node with storage and interconnect specs. | _required_ |
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#### Returns {.doc-section .doc-section-returns}
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| Name | Type | Description |
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|--------|------------------|---------------------------------------------------------------|
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| | Dict\[str, Any\] | Pipeline metrics including utilization and stall probability. |
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