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cs249r_book/mlsysim/docs/api/index.qmd
Vijay Janapa Reddi c53f3fe238 Site QA polish: finish MLSys.im brand migration, fix broken links, add memory-tier figure 
- Complete the MLSYSIM -> MLSys.im display-name migration across mlsysim/docs,
  instructors, and shared config (code identifiers stay lowercase mlsysim)
- Fix broken TinyTorch module links (_ABOUT.html -> .html)
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- Add the Accelerator Memory Tiers figure to compute_infrastructure with a
  registry-driven log-log capacity/bandwidth scatter
- Add four sourced cloud accelerator specs (Groq LPU, Graphcore GC200,
  Untether speedAI240, d-Matrix Corsair) feeding the local-SRAM tier
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---
title: "API Reference"
description: "Public API surface for MLSys·im models, hardware registries, solvers, and analytical engines."
---
## Core API
Primary objects and resolvers.
| | |
| --- | --- |
| [hardware](hardware.qmd#mlsysim.hardware) | |
| [models](models.qmd#mlsysim.models) | |
| [infrastructure](infrastructure.qmd#mlsysim.infrastructure) | |
| [systems](systems.qmd#mlsysim.systems) | |
| [platforms](platforms.qmd#mlsysim.platforms) | Platform deployment envelopes. |
| [datasets](datasets.qmd#mlsysim.datasets) | Dataset zoo — canonical data corpus profiles. |
| [literature](literature.qmd#mlsysim.literature) | |
| [ops](ops.qmd#mlsysim.ops) | |
| [core](core.qmd#mlsysim.core) | |
| [engine](engine.qmd#mlsysim.engine) | |
| [solvers](solvers.qmd#mlsysim.solvers) | Canonical public solver import surface. |
| [core.provenance.Provenance](core.provenance.Provenance.qmd#mlsysim.core.provenance.Provenance) | How we know a numeric value (package audit trail; not BibTeX). |
| [core.provenance.ProvenanceKind](core.provenance.ProvenanceKind.qmd#mlsysim.core.provenance.ProvenanceKind) | |
| [core.provenance.Sourced](core.provenance.Sourced.qmd#mlsysim.core.provenance.Sourced) | Scalar with mandatory ``Provenance``. Subclasses ``float`` so appendix |
| [engine.calibration](engine.calibration.qmd#mlsysim.engine.calibration) | Parameters for analytical solvers and the roofline engine. |
| [fmt.fmt](fmt.fmt.qmd#mlsysim.fmt.fmt) | Format a Pint Quantity (or plain number) for narrative text. |
| [fmt.fmt_int](fmt.fmt_int.qmd#mlsysim.fmt.fmt_int) | Format a value as an integer for narrative text. |
| [physics](physics.qmd#mlsysim.physics) | Canonical physics and accounting formulas for ML systems. |
| [solvers.SingleNodeModel](solvers.SingleNodeModel.qmd#mlsysim.solvers.SingleNodeModel) | Resolves single-node hardware Roofline bounds and feasibility. |
| [solvers.NetworkRooflineModel](solvers.NetworkRooflineModel.qmd#mlsysim.solvers.NetworkRooflineModel) | Analyzes the Distributed Performance Bounds (The Network Wall). |
| [solvers.EfficiencyModel](solvers.EfficiencyModel.qmd#mlsysim.solvers.EfficiencyModel) | Models the gap between peak and achieved FLOPS (Wall 3: Software Efficiency). |
| [solvers.ForwardModel](solvers.ForwardModel.qmd#mlsysim.solvers.ForwardModel) | Forward-evaluating mechanistic engine (Y = f(X)). |
| [solvers.ServingModel](solvers.ServingModel.qmd#mlsysim.solvers.ServingModel) | Analyzes the two-phase LLM serving lifecycle: Pre-fill vs. Decoding. |
| [solvers.TrainingMemoryModel](solvers.TrainingMemoryModel.qmd#mlsysim.solvers.TrainingMemoryModel) | Decomposes per-accelerator training memory into teachable components. |
| [solvers.ServingCapacityModel](solvers.ServingCapacityModel.qmd#mlsysim.solvers.ServingCapacityModel) | Sizes an LLM serving deployment from a QPS and tail-latency target. |
| [solvers.ContinuousBatchingModel](solvers.ContinuousBatchingModel.qmd#mlsysim.solvers.ContinuousBatchingModel) | Analyzes production LLM serving with Continuous Batching and PagedAttention. |
| [solvers.WeightStreamingModel](solvers.WeightStreamingModel.qmd#mlsysim.solvers.WeightStreamingModel) | Analyzes Wafer-Scale inference (e.g., Cerebras CS-3) using Weight Streaming. |
| [solvers.TailLatencyModel](solvers.TailLatencyModel.qmd#mlsysim.solvers.TailLatencyModel) | Analyzes queueing delays and P99 tail latency for deployed inference models. |
| [solvers.DataModel](solvers.DataModel.qmd#mlsysim.solvers.DataModel) | Analyzes the 'Data Wall' — the throughput bottleneck between storage and compute. |
| [solvers.TransformationModel](solvers.TransformationModel.qmd#mlsysim.solvers.TransformationModel) | Quantifies the CPU preprocessing bottleneck (Wall 9: Transformation). |
| [solvers.TopologyModel](solvers.TopologyModel.qmd#mlsysim.solvers.TopologyModel) | Models bisection bandwidth for different network topologies (Wall 10). |
| [solvers.ScalingModel](solvers.ScalingModel.qmd#mlsysim.solvers.ScalingModel) | Analyzes the 'Scaling Physics' of model training (Chinchilla Laws). |
| [solvers.InferenceScalingModel](solvers.InferenceScalingModel.qmd#mlsysim.solvers.InferenceScalingModel) | Models inference-time compute scaling (Wall 12: Reasoning/CoT Cost). |
| [solvers.CompressionModel](solvers.CompressionModel.qmd#mlsysim.solvers.CompressionModel) | Analyzes model compression trade-offs (Accuracy vs. Efficiency). |
| [solvers.DistributedModel](solvers.DistributedModel.qmd#mlsysim.solvers.DistributedModel) | Resolves fleet-wide communication, synchronization, and pipelining constraints. |
| [solvers.MoERoutingModel](solvers.MoERoutingModel.qmd#mlsysim.solvers.MoERoutingModel) | Models first-order MoE routing imbalance and expert-parallel all-to-all cost. |
| [solvers.ReliabilityModel](solvers.ReliabilityModel.qmd#mlsysim.solvers.ReliabilityModel) | Calculates Mean Time Between Failures (MTBF) and optimal checkpointing intervals. |
| [solvers.OrchestrationModel](solvers.OrchestrationModel.qmd#mlsysim.solvers.OrchestrationModel) | Analyzes Cluster Orchestration and Queueing (Little's Law). |
| [solvers.EconomicsModel](solvers.EconomicsModel.qmd#mlsysim.solvers.EconomicsModel) | Calculates Total Cost of Ownership (TCO) including Capex and Opex. |
| [solvers.SustainabilityModel](solvers.SustainabilityModel.qmd#mlsysim.solvers.SustainabilityModel) | Calculates Datacenter-scale Sustainability metrics. |
| [solvers.CheckpointModel](solvers.CheckpointModel.qmd#mlsysim.solvers.CheckpointModel) | Analyzes the storage constraints and I/O burst penalties of saving model states. |
| [solvers.ResponsibleEngineeringModel](solvers.ResponsibleEngineeringModel.qmd#mlsysim.solvers.ResponsibleEngineeringModel) | Models the computational cost of responsible AI practices (Wall 20: Safety). |
| [solvers.SensitivitySolver](solvers.SensitivitySolver.qmd#mlsysim.solvers.SensitivitySolver) | Identifies the binding constraint via numerical sensitivity analysis (Wall 21). |
| [solvers.SynthesisSolver](solvers.SynthesisSolver.qmd#mlsysim.solvers.SynthesisSolver) | Given an SLA, synthesizes the required hardware specs (Wall 22: Inverse Solve). |
| [solvers.ParallelismOptimizer](solvers.ParallelismOptimizer.qmd#mlsysim.solvers.ParallelismOptimizer) | Searches for the optimal 3D/4D parallelism split (DP, TP, PP, EP). |
| [solvers.BatchingOptimizer](solvers.BatchingOptimizer.qmd#mlsysim.solvers.BatchingOptimizer) | Finds the maximum batch size that satisfies a P99 latency SLA. |
| [solvers.PlacementOptimizer](solvers.PlacementOptimizer.qmd#mlsysim.solvers.PlacementOptimizer) | Finds the optimal datacenter location to minimize TCO and Carbon. |
| [engine.dse.DSE](engine.dse.DSE.qmd#mlsysim.engine.dse.DSE) | Declarative Design Space Exploration (DSE) Engine. |
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