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Expand walls.py from 17 to 22 walls, adding Serving (4), Batching (5), Streaming (6), Tail Latency (7), and Checkpoint (19). Update paper.tex with rewritten abstract, concrete LLaMA-3 motivating example, competitive positioning against Calculon/ASTRA-sim/Vidur, and new overview figure. Rebrand docs and tutorials to match.
15 lines
493 B
Plaintext
15 lines
493 B
Plaintext
# core.solver.SynthesisSolver { #mlsysim.core.solver.SynthesisSolver }
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```python
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core.solver.SynthesisSolver()
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```
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The 'Architect's Solver' — synthesizes ideal hardware for a model.
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This solver performs the 'Inverse Solve': given a workload and an SLA,
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what are the required memory bandwidth and peak compute specs?
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Literature Source:
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1. Jouppi et al. (2017), "In-Datacenter Performance Analysis of a TPU."
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2. Horowitz (2014), "Computing's Energy Problem (and what we can do about it)."
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