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AI Architecture › L0 · Foundation
L0 · Foundation

Compute Scheduling and Orchestration

How L0 packs Monte Carlo, coupled solves, and GPU training onto a shared pool without starving the reproducible core.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L0 · FOUNDATIONThe offline compute substrate — multi-physics & batch training.1Cloud HPCelastic burst2Bare-Metal ClusterGPU / CPU3Supercomputingmulti-physics runs4Batch Trainingmodel builds5Simulation FarmGrad-Shafranov · MHD6Object StorecheckpointsMACHINE TIETrains the models that ship UP to L3 — no real-time path to the machine.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORFOUNDATIONSHEET 02REV. 2026-08L0 · AI-NATIVE STACK
L0 · Foundation — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Three workload shapes, one queue discipline

L0 runs three very different shapes on one pool: throughput-parallel Monte Carlo, latency-bound coupled solves, and GPU training. A single fair-share scheduler cannot treat them alike. Kronos orchestration classifies each job by shape and reproducibility tier, then places it where it will neither waste the pool nor collide with sensitive work.

Backfill for the wide frontier

Monte Carlo and hyperparameter sweeps are ideal backfill: they are independent, checkpointable, and preemptible. The scheduler uses them to fill gaps left by large coupled solves, keeping utilization high without delaying tightly scheduled reproducible runs. A breeder TBR sweep can be paused, migrated, and resumed with no scientific loss.

Reservations for the reproducible core

Certified reproducible runs, the coupled MHD-thermomechanics solves that seed twin surrogates, get reserved placement on pinned nodes. They are never co-scheduled with noisy neighbors that could perturb timing or numerics. The scheduler treats reproducibility as a hard constraint, not a preference.

Coupled solves and distributed training both need gang scheduling: all ranks start together or not at all, because a partial allocation deadlocks a synchronous job. Orchestration allocates these atomically and holds them until the full set of nodes and the required fabric topology are available.

The scheduler is machine-blind by design. A breeder equilibrium campaign and a burner end-cell study compete for the pool on equal terms, arbitrated by shape, tier, and priority. This keeps both machines' study cadences moving without either one monopolizing the substrate.

Content reviewed August 2026 · design-and-simulation stage