mirror of
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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.)
56 lines
6.9 KiB
Plaintext
56 lines
6.9 KiB
Plaintext
# API Reference {.doc .doc-index}
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## Core API
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Primary objects and resolvers.
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| --- | --- |
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| [hardware](hardware.qmd#mlsysim.hardware) | |
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| [models](models.qmd#mlsysim.models) | |
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| [infrastructure](infrastructure.qmd#mlsysim.infrastructure) | |
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| [systems](systems.qmd#mlsysim.systems) | |
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| [platforms](platforms.qmd#mlsysim.platforms) | Platform deployment envelopes. |
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| [datasets](datasets.qmd#mlsysim.datasets) | Dataset zoo — canonical data corpus profiles. |
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| [literature](literature.qmd#mlsysim.literature) | |
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| [ops](ops.qmd#mlsysim.ops) | |
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| [core](core.qmd#mlsysim.core) | |
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| [engine](engine.qmd#mlsysim.engine) | |
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| [core.provenance.Provenance](core.provenance.Provenance.qmd#mlsysim.core.provenance.Provenance) | How we know a numeric value (package audit trail; not BibTeX). |
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| [core.provenance.ProvenanceKind](core.provenance.ProvenanceKind.qmd#mlsysim.core.provenance.ProvenanceKind) | |
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| [core.provenance.Sourced](core.provenance.Sourced.qmd#mlsysim.core.provenance.Sourced) | Scalar with mandatory ``Provenance``. Subclasses ``float`` so appendix |
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| [engine.calibration](engine.calibration.qmd#mlsysim.engine.calibration) | Parameters for analytical solvers and the roofline engine. |
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| [fmt.fmt](fmt.fmt.qmd#mlsysim.fmt.fmt) | Format a Pint Quantity (or plain number) for narrative text. |
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| [fmt.fmt_int](fmt.fmt_int.qmd#mlsysim.fmt.fmt_int) | Format a value as an integer for narrative text. |
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| [physics](physics.qmd#mlsysim.physics) | Canonical physics and accounting formulas for ML systems. |
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| [engine.solver.SingleNodeModel](engine.solver.SingleNodeModel.qmd#mlsysim.engine.solver.SingleNodeModel) | Resolves single-node hardware Roofline bounds and feasibility. |
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| [engine.solver.NetworkRooflineModel](engine.solver.NetworkRooflineModel.qmd#mlsysim.engine.solver.NetworkRooflineModel) | Analyzes the Distributed Performance Bounds (The Network Wall). |
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| [engine.solver.EfficiencyModel](engine.solver.EfficiencyModel.qmd#mlsysim.engine.solver.EfficiencyModel) | Models the gap between peak and achieved FLOPS (Wall 3: Software Efficiency). |
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| [engine.solver.ForwardModel](engine.solver.ForwardModel.qmd#mlsysim.engine.solver.ForwardModel) | Forward-evaluating mechanistic engine (Y = f(X)). |
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| [engine.solver.ServingModel](engine.solver.ServingModel.qmd#mlsysim.engine.solver.ServingModel) | Analyzes the two-phase LLM serving lifecycle: Pre-fill vs. Decoding. |
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| [engine.solver.TrainingMemoryModel](engine.solver.TrainingMemoryModel.qmd#mlsysim.engine.solver.TrainingMemoryModel) | Decomposes per-accelerator training memory into teachable components. |
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| [engine.solver.ServingCapacityModel](engine.solver.ServingCapacityModel.qmd#mlsysim.engine.solver.ServingCapacityModel) | Sizes an LLM serving deployment from a QPS and tail-latency target. |
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| [engine.solver.ContinuousBatchingModel](engine.solver.ContinuousBatchingModel.qmd#mlsysim.engine.solver.ContinuousBatchingModel) | Analyzes production LLM serving with Continuous Batching and PagedAttention. |
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| [engine.solver.WeightStreamingModel](engine.solver.WeightStreamingModel.qmd#mlsysim.engine.solver.WeightStreamingModel) | Analyzes Wafer-Scale inference (e.g., Cerebras CS-3) using Weight Streaming. |
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| [engine.solver.TailLatencyModel](engine.solver.TailLatencyModel.qmd#mlsysim.engine.solver.TailLatencyModel) | Analyzes queueing delays and P99 tail latency for deployed inference models. |
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| [engine.solver.DataModel](engine.solver.DataModel.qmd#mlsysim.engine.solver.DataModel) | Analyzes the 'Data Wall' — the throughput bottleneck between storage and compute. |
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| [engine.solver.TransformationModel](engine.solver.TransformationModel.qmd#mlsysim.engine.solver.TransformationModel) | Quantifies the CPU preprocessing bottleneck (Wall 9: Transformation). |
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| [engine.solver.TopologyModel](engine.solver.TopologyModel.qmd#mlsysim.engine.solver.TopologyModel) | Models bisection bandwidth for different network topologies (Wall 10). |
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| [engine.solver.ScalingModel](engine.solver.ScalingModel.qmd#mlsysim.engine.solver.ScalingModel) | Analyzes the 'Scaling Physics' of model training (Chinchilla Laws). |
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| [engine.solver.InferenceScalingModel](engine.solver.InferenceScalingModel.qmd#mlsysim.engine.solver.InferenceScalingModel) | Models inference-time compute scaling (Wall 12: Reasoning/CoT Cost). |
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| [engine.solver.CompressionModel](engine.solver.CompressionModel.qmd#mlsysim.engine.solver.CompressionModel) | Analyzes model compression trade-offs (Accuracy vs. Efficiency). |
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| [engine.solver.DistributedModel](engine.solver.DistributedModel.qmd#mlsysim.engine.solver.DistributedModel) | Resolves fleet-wide communication, synchronization, and pipelining constraints. |
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| [engine.solver.MoERoutingModel](engine.solver.MoERoutingModel.qmd#mlsysim.engine.solver.MoERoutingModel) | Models first-order MoE routing imbalance and expert-parallel all-to-all cost. |
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| [engine.solver.ReliabilityModel](engine.solver.ReliabilityModel.qmd#mlsysim.engine.solver.ReliabilityModel) | Calculates Mean Time Between Failures (MTBF) and optimal checkpointing intervals. |
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| [engine.solver.OrchestrationModel](engine.solver.OrchestrationModel.qmd#mlsysim.engine.solver.OrchestrationModel) | Analyzes Cluster Orchestration and Queueing (Little's Law). |
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| [engine.solver.EconomicsModel](engine.solver.EconomicsModel.qmd#mlsysim.engine.solver.EconomicsModel) | Calculates Total Cost of Ownership (TCO) including Capex and Opex. |
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| [engine.solver.SustainabilityModel](engine.solver.SustainabilityModel.qmd#mlsysim.engine.solver.SustainabilityModel) | Calculates Datacenter-scale Sustainability metrics. |
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| [engine.solver.CheckpointModel](engine.solver.CheckpointModel.qmd#mlsysim.engine.solver.CheckpointModel) | Analyzes the storage constraints and I/O burst penalties of saving model states. |
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| [engine.solver.ResponsibleEngineeringModel](engine.solver.ResponsibleEngineeringModel.qmd#mlsysim.engine.solver.ResponsibleEngineeringModel) | Models the computational cost of responsible AI practices (Wall 20: Safety). |
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| [engine.solver.SensitivitySolver](engine.solver.SensitivitySolver.qmd#mlsysim.engine.solver.SensitivitySolver) | Identifies the binding constraint via numerical sensitivity analysis (Wall 21). |
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| [engine.solver.SynthesisSolver](engine.solver.SynthesisSolver.qmd#mlsysim.engine.solver.SynthesisSolver) | Given an SLA, synthesizes the required hardware specs (Wall 22: Inverse Solve). |
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| [engine.solver.ParallelismOptimizer](engine.solver.ParallelismOptimizer.qmd#mlsysim.engine.solver.ParallelismOptimizer) | Searches for the optimal 3D/4D parallelism split (DP, TP, PP, EP). |
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| [engine.solver.BatchingOptimizer](engine.solver.BatchingOptimizer.qmd#mlsysim.engine.solver.BatchingOptimizer) | Finds the maximum batch size that satisfies a P99 latency SLA. |
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| [engine.solver.PlacementOptimizer](engine.solver.PlacementOptimizer.qmd#mlsysim.engine.solver.PlacementOptimizer) | Finds the optimal datacenter location to minimize TCO and Carbon. |
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| [engine.dse.DSE](engine.dse.DSE.qmd#mlsysim.engine.dse.DSE) | Declarative Design Space Exploration (DSE) Engine. |
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