Engineering / advanced
Allocate shared cluster resources
Analyze packing, fairness, topology, gang scheduling, preemption, and fragmentation for heterogeneous shared compute clusters.
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Module 01 advanced
Allocate shared cluster resources
Analyze packing, fairness, topology, gang scheduling, preemption, and fragmentation for heterogeneous shared compute clusters.
- 01
Resource requests and feasibility
Separate admission accounting, runtime consumption, and enforcement while checking every required resource dimension.
25 min - 02
Multidimensional resource packing
Use lower bounds, explicit assignments, and heuristic limits instead of equating a good score with an optimal placement.
25 min - 03
Dominant resource fairness
Normalize heterogeneous resource demands, compare dominant shares, and state the assumptions behind a fairness policy.
25 min - 04
Topology and affinity constraints
Treat device locality, communication, and failure-domain requirements as explicit constraints with measurable tradeoffs.
25 min - 05
Gang scheduling and co-scheduling
Coordinate multi-worker admission while distinguishing resource readiness from synchronized execution and eventual job progress.
25 min - 06
Preemption and victim selection
Release the right feasible resources while accounting for disruption, checkpointing, lost progress, and policy boundaries.
25 min - 07
Accelerator resource fragmentation
Separate device count, per-device memory, partition profiles, and useful compute before attempting consolidation.
25 min - 08
Queue fairness and aging
Define waiting, entitlement, and job progress explicitly while evaluating starvation and large-job admission.
25 min - 09
Reservations and capacity borrowing
Define entitlements, unused quota, revocation, and physical placement instead of treating borrowed resources as permanent capacity.
25 min - 10
Scheduler simulation and trace replay
Calibrate workload and cost models, compare policies consistently, and preserve the limits of counterfactual conclusions.
25 min