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https://github.com/harvard-edge/cs249r_book.git
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Vijay Janapa Reddi
cb0ae4082f
Run quartodoc build against the refactored package (engine/ extracted from core/, infra->infrastructure): regenerate the API reference so every stub points at the new paths. Adds engine.*/infrastructure.* stubs, removes the 41 stale core.solver.* / core.engine.* / core.scenarios.* / core.config.* / core.evaluation.* / infra*.qmd orphans, and refreshes the package pages (core now primitives-only) + index. Verified: all 47 documented symbols resolve in the new package; every index link resolves; zero stale mlsysim.core.<engine-mod> / mlsysim.infra references anywhere in docs/. (objects.json inventory is gitignored -- regenerated at build.)
40 lines
1.2 KiB
Plaintext
40 lines
1.2 KiB
Plaintext
# engine.solver.ContinuousBatchingModel { #mlsysim.engine.solver.ContinuousBatchingModel }
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```python
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engine.solver.ContinuousBatchingModel()
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```
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Analyzes production LLM serving with Continuous Batching and PagedAttention.
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Traditional static batching suffers from severe memory fragmentation and
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padding waste. This model simulates the throughput improvements achieved by
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iteration-level scheduling and non-contiguous KV cache allocation.
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Literature Source:
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1. Kwon et al. (2023), "Efficient Memory Management for Large Language Model
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Serving with PagedAttention."
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2. Yu et al. (2022), "ORCA: A Distributed Serving System for
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Transformer-Based Generative Models."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.engine.solver.ContinuousBatchingModel.solve) | Calculates continuous batching throughput and PagedAttention memory. |
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### solve { #mlsysim.engine.solver.ContinuousBatchingModel.solve }
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```python
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engine.solver.ContinuousBatchingModel.solve(
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model,
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hardware,
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seq_len,
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max_batch_size=1,
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page_size=16,
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precision='fp16',
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efficiency=0.5,
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)
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```
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Calculates continuous batching throughput and PagedAttention memory.
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