GPU Cluster Ontology

FLOW ALGEBRA
GPU Cluster Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/artificial-intelligence-infrastructure/gpu-cluster-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "GPUClusterOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := ArtificialIntelligenceInfrastructure
ONTOLOGY := GPUClusterOntology

# ---------- PRIMARY ENTITY ----------
ENTITY GPUCluster {
    definition:
        "A coordinated collection of compute nodes containing graphics
         processing units or related accelerators, connected through high-speed
         networks and managed as a shared platform for parallel artificial
         intelligence, scientific and high-performance computing workloads.";

    objectives: [
        ParallelCompute,
        DistributedTraining,
        AcceleratorSharing,
        HighThroughputProcessing,
        Scalability,
        FaultTolerance,
        EfficientResourceUtilisation
    ];
}

# ---------- CORE ENTITIES ----------
ClusterEntity := {
    GPUCluster,
    ComputeNode,
    GPU,
    CPU,
    Memory,
    Storage,
    Network,
    Interconnect
};

ManagementEntity := {
    Scheduler,
    Orchestrator,
    ResourceManager,
    NodeAgent,
    MonitoringSystem,
    JobQueue
};

WorkloadEntity := {
    TrainingJob,
    InferenceJob,
    BatchJob,
    DistributedJob,
    InteractiveJob
};

ClusterState := {
    Available,
    PartiallyAllocated,
    FullyAllocated,
    Degraded,
    Maintenance,
    Failed
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
GPUCluster CONSISTS_OF ComputeNode;
ComputeNode CONTAINS GPU;
ComputeNode CONTAINS CPU;
ComputeNode HAS HostMemory;
GPU HAS DeviceMemory;
ComputeNode CONNECTED_BY ClusterNetwork;
Scheduler ALLOCATES GPU TO Workload;
MonitoringSystem OBSERVES NodeAndGPUState;

# ---------- CLUSTER EXECUTION FLOW ----------
WorkloadRequest
    -> SubmitJob
    -> ValidateResourceRequest
    -> QueueJob
    -> IdentifyEligibleNodes
    -> AllocateGPUs
    -> ConfigureRuntime
    -> LaunchWorkers
    -> ExecuteParallelWorkload
    -> MonitorExecution
    -> ReleaseResources
    -> RecordMetrics;

# ---------- COMPUTE NODE ----------
ComputeNode :=
    {
        CPU,
        HostMemory,
        GPUSet,
        NetworkInterface,
        LocalStorage
    };

NodeCapacity :=
    f(
        GPUCount,
        GPUType,
        HostMemory,
        CPUCount,
        NetworkBandwidth
    );

# ---------- GPU ----------
GPU :=
    ParallelComputeAccelerator;

GPU MAY_HAVE {
    DeviceMemory,
    ComputeUnits,
    TensorComputeUnits,
    MemoryBandwidth,
    InterconnectLinks
};

GPUState MAY_BE {
    Idle,
    Allocated,
    Busy,
    Degraded,
    Failed
};

# ---------- DEVICE MEMORY ----------
DeviceMemory :=
    HighBandwidthMemoryAccessibleByGPU;

DeviceMemory MAY_STORE {
    ModelParameters,
    Activations,
    Gradients,
    InputBatch,
    Workspace
};

IF WorkloadMemoryRequirement > AvailableDeviceMemory
THEN Workload MAY_REQUIRE ShardingOffloadOrSmallerBatch;

# ---------- INTRA-NODE INTERCONNECT ----------
IntraNodeInterconnect :=
    HighBandwidthConnectionBetweenGPUsWithinNode;

Interconnect MAY_SUPPORT {
    PeerToPeerTransfer,
    CollectiveCommunication
};

HigherInterGPUBandwidth MAY_IMPROVE MultiGPUCommunication;

# ---------- INTER-NODE NETWORK ----------
ClusterNetwork :=
    NetworkConnectingComputeNodes;

NetworkProperty := {
    Bandwidth,
    Latency,
    Topology,
    Congestion,
    Reliability
};

DistributedWorkloadPerformance MAY_DEPEND_ON ClusterNetwork;

# ---------- TOPOLOGY ----------
ClusterTopology :=
    ArrangementOfNodesGPUsAndNetworkLinks;

Topology MAY_AFFECT {
    CommunicationCost,
    Placement,
    ScalingEfficiency
};

TopologyAwareScheduling MAY_PLACE CloselyCommunicatingWorkersNearerInNetworkTopology;

# ---------- STORAGE ----------
ClusterStorage MAY_INCLUDE {
    DistributedFileSystem,
    ObjectStorage,
    SharedFilesystem,
    LocalNVMe,
    CheckpointStore
};

Workload MAY_READ {
    Dataset,
    ModelArtifact,
    ContainerImage
};

Workload MAY_WRITE {
    Checkpoint,
    Output,
    Log,
    ModelArtifact
};

# ---------- JOB ----------
ClusterJob :=
    {
        WorkloadType,
        ResourceRequest,
        RuntimeEnvironment,
        InputData,
        Priority
    };

JobState MAY_BE {
    Pending,
    Scheduled,
    Running,
    Completed,
    Failed,
    Cancelled
};

# ---------- RESOURCE REQUEST ----------
GPUResourceRequest MAY_INCLUDE {
    GPUCount,
    GPUType,
    GPUmemory,
    NodeCount,
    CPU,
    HostMemory,
    NetworkRequirement
};

# ---------- SCHEDULER ----------
ClusterScheduler :=
    JobQueue
    + ResourceInventory
    + SchedulingPolicy
    -> Allocation;

SchedulingPolicy MAY_CONSIDER {
    Priority,
    FairShare,
    Quota,
    Topology,
    GPUType,
    Locality
};

# ---------- PLACEMENT ----------
Placement :=
    MapWorkloadWorkersToNodesAndGPUs;

Placement SHOULD_SATISFY ResourceRequirements;

Placement MAY_OPTIMISE {
    CommunicationLocality,
    DataLocality,
    ResourceUtilisation
};

# ---------- GPU ALLOCATION ----------
GPUAllocation :=
    ReserveSelectedGPUDevicesForWorkload;

ExclusiveAllocation MAY_ASSIGN GPUToSingleWorkload;
SharedAllocation MAY_PARTITION_OR_SHARE GPUWhereSupported;

# ---------- GPU PARTITIONING ----------
GPU MAY_SUPPORT HardwareOrSoftwarePartitioning;

Partition :=
    SubdivisionOfGPUResourcesForIndependentWorkloads;

Partitioning MAY_IMPROVE UtilisationForSmallerWorkloads;

# ---------- CONTAINER ----------
GPUWorkload MAY_RUN_IN Container;

ContainerRuntime MUST_EXPOSE AllocatedGPUDevicesToWorkload;

ContainerImage MAY_INCLUDE {
    MLFramework,
    GPUDriversInterfaceLibraries,
    ApplicationCode,
    Dependencies
};

# ---------- DRIVER ----------
GPUDriver :=
    SystemSoftwareProvidingHostAccessToGPU;

RuntimeCompatibility DEPENDS_ON {
    Driver,
    AcceleratorRuntime,
    Framework,
    Hardware
};

# ---------- DISTRIBUTED RUNTIME ----------
DistributedRuntime :=
    MultipleWorkers
    + CommunicationBackend
    + CoordinatedExecution;

Worker MAY_MAP_TO {
    GPU,
    Process,
    Node
};

# ---------- COLLECTIVE COMMUNICATION ----------
CollectiveOperation MAY_INCLUDE {
    AllReduce,
    AllGather,
    ReduceScatter,
    Broadcast
};

CollectiveCommunication COORDINATES DataAcrossWorkers;

# ---------- ALL-REDUCE ----------
AllReduce :=
    EachWorkerProvidesValue
    -> CombineValues
    -> ReturnCombinedResultToAllWorkers;

DistributedTraining OFTEN_USES AllReduceForGradientAggregation;

# ---------- DATA PARALLEL TRAINING ----------
DataParallelTraining :=
    ReplicateModelAcrossGPUs
    -> PartitionBatch
    -> ComputeLocalGradients
    -> AggregateGradients
    -> UpdateModel;

# ---------- TENSOR PARALLELISM ----------
TensorParallelism :=
    PartitionTensorOperationAcrossMultipleGPUs;

TensorParallelism MAY_REQUIRE FrequentHighBandwidthCommunication;

# ---------- PIPELINE PARALLELISM ----------
PipelineParallelism :=
    PartitionModelStagesAcrossGPUsOrNodes
    -> TransferActivations
    -> ExecuteMicrobatches;

# ---------- HYBRID PARALLELISM ----------
HybridParallelism :=
    CombinationOf {
        DataParallelism,
        TensorParallelism,
        PipelineParallelism
    };

LargeModelTraining MAY_REQUIRE HybridParallelism;

# ---------- COMMUNICATION COST ----------
CommunicationCost :=
    f(
        MessageSize,
        LinkBandwidth,
        LinkLatency,
        Topology,
        Contention
    );

IF CommunicationCostDominatesCompute
THEN ScalingEfficiency := decrease;

# ---------- GPU UTILISATION ----------
GPUUtilisation :=
    UsefulGPUActivity / AvailableGPUCapacity;

LowGPUUtilisation MAY_BE_CAUSED_BY {
    DataLoadingDelay,
    CPUPreprocessing,
    CommunicationWait,
    SmallWorkload,
    Synchronisation
};

# ---------- MEMORY UTILISATION ----------
GPUMemoryUtilisation :=
    AllocatedOrUsedDeviceMemory / DeviceMemoryCapacity;

HighMemoryPressure MAY_LIMIT BatchSizeOrModelPlacement;

# ---------- JOB QUEUE ----------
JobQueue STORES PendingJobs;

QueueTime :=
    JobStartTime - JobSubmissionTime;

QueueTime MAY_INCREASE_IF ResourceDemandExceedsCapacity;

# ---------- FAIR SHARE ----------
FairShareScheduling :=
    AllocateResourcesConsideringHistoricalOrConfiguredUsage;

FairShare MAY_PREVENT SingleUserOrTeamFromMonopolisingCluster;

# ---------- QUOTA ----------
ResourceQuota MAY_LIMIT {
    GPUCount,
    ConcurrentJobs,
    NodeCount,
    CPU,
    Memory
};

# ---------- PREEMPTION ----------
Preemption :=
    SuspendOrTerminateLowerPriorityWorkloadToFreeResources;

PreemptibleJob SHOULD_SUPPORT RecoveryWhereRequired;

# ---------- CHECKPOINT ----------
Checkpoint :=
    PersistedWorkloadState;

TrainingJob MAY_SAVE Checkpoint;

IF JobPreemptedOrFails
AND ValidCheckpointExists
THEN Job MAY_RESUME;

# ---------- FAULT ----------
ClusterFailure MAY_INCLUDE {
    GPUFailure,
    NodeFailure,
    NetworkFailure,
    StorageFailure,
    SchedulerFailure,
    PowerFailure
};

# ---------- NODE HEALTH ----------
NodeHealth MAY_BE {
    Healthy,
    Degraded,
    Unhealthy,
    Drained
};

HealthCheck MAY_EVALUATE {
    GPUStatus,
    NetworkStatus,
    StorageStatus,
    Temperature,
    ProcessHealth
};

# ---------- DRAINING ----------
NodeDrain :=
    PreventNewWorkloads
    + AllowOrMoveExistingWorkloads
    -> MaintenanceOrRepair;

# ---------- RESCHEDULING ----------
IF NodeFails
THEN Scheduler MAY_RESCHEDULE RecoverableWorkloadToHealthyNode;

StatefulTraining MAY_REQUIRE CheckpointForEfficientRecovery;

# ---------- MONITORING ----------
ClusterMonitoring MAY_COLLECT {
    GPUUtilisation,
    GPUMemoryUse,
    Temperature,
    Power,
    NetworkThroughput,
    StorageThroughput,
    JobState,
    QueueDepth
};

# ---------- TELEMETRY ----------
GPUTelemetry MAY_INCLUDE {
    DeviceUtilisation,
    MemoryUtilisation,
    Temperature,
    PowerDraw,
    ErrorCounters
};

# ---------- OBSERVABILITY ----------
ClusterObservability :=
    Metrics
    + Logs
    + Events
    + Traces;

Observability SUPPORTS CapacityPlanningAndFailureDiagnosis;

# ---------- PERFORMANCE ----------
ClusterPerformance MAY_BE_MEASURED_BY {
    AggregateComputeThroughput,
    JobCompletionTime,
    ScalingEfficiency,
    GPUUtilisation,
    QueueTime,
    NetworkUtilisation
};

# ---------- SCALING ----------
StrongScaling :=
    FixedWorkload
    + IncreasingGPUCount;

WeakScaling :=
    IncreaseWorkloadWithGPUCount;

ScalingEfficiency MAY_DECLINE_DUE_TO {
    Communication,
    Synchronisation,
    LoadImbalance
};

# ---------- CAPACITY PLANNING ----------
GPUCapacityPlanning :=
    ForecastWorkloadDemand
    -> EstimateGPUHours
    -> EstimateNodeAndNetworkCapacity
    -> ProvisionOrReserveResources;

# ---------- ENERGY ----------
ClusterPower :=
    SUM(NodePower);

EnergyToSolution :=
    ClusterPowerOverTimeIntegratedAcrossJobRuntime;

HigherUtilisation MAY_IMPROVE WorkPerEnergyUnderSuitableConditions;

# ---------- COOLING ----------
GPUClusterInfrastructure REQUIRES ThermalManagement;

CoolingCapacity MUST_SUPPORT ExpectedHeatLoad;

ThermalConstraint MAY_LIMIT SustainedPerformance;

# ---------- SECURITY ----------
GPUClusterSecurity MAY_INCLUDE {
    Identity,
    Authorisation,
    NetworkSegmentation,
    WorkloadIsolation,
    SecretManagement,
    StorageAccessControl,
    Audit
};

# ---------- MULTI-TENANCY ----------
MultiTenantCluster :=
    ClusterSharedByMultipleUsersTeamsOrWorkloads;

MultiTenancy REQUIRES {
    ResourceIsolation,
    AccessControl,
    FairScheduling,
    Quotas
};

# ---------- DATA LOCALITY ----------
DataLocality :=
    ProximityOfWorkloadToRequiredData;

BetterDataLocality MAY_REDUCE DataTransferTime;

# ---------- MODEL LOCALITY ----------
ModelArtifact MAY_BE CachedNearComputeNodes;

LocalModelCache MAY_REDUCE StartupTimeForRepeatedWorkloads;

# ---------- SEMANTIC REASONING ----------
IF MultipleGPUComputeNodesManagedAsSharedParallelPlatform
THEN GPUClusterClassification := supported;

IF WorkloadRequiresMoreGPUsThanAnySingleNodeContains
THEN MultiNodePlacement := required;

IF InterNodeCommunicationHigh
THEN NetworkTopologyAndBandwidth BecomeCritical;

IF GPUUtilisationLow
AND DataLoaderTimeHigh
THEN DataPipelineBottleneck := likely;

IF GPUFailureDetected
THEN Device SHOULD_BE RemovedFromNormalSchedulingUntilHealthy;

IF QueueDepthPersistentlyHigh
THEN ClusterCapacity MAY_BE Insufficient;

IF ValidCheckpointExistsAfterNodeFailure
THEN TrainingRecovery MAY_BE Possible;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "GPU cluster" ~= "graphics processing unit compute cluster";
    "accelerator cluster" ~= "GPU or AI accelerator cluster" WHEN context == AI_compute;
    "GPU node" ~= "GPU compute node";
    "GPU scheduler" ~= "accelerator resource scheduler";
    "multi-GPU cluster" ~= "GPU cluster";
    "collective communication" ~= "distributed GPU collective operations";
    "GPU fabric" ~= "high-speed GPU interconnect fabric";
    "GPU pool" ~= "shared accelerator resource pool";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_CLUSTER_AND_NODES(q) ->
    IDENTIFY_GPUS_AND_MEMORY(q) ->
    IDENTIFY_NETWORK_AND_TOPOLOGY(q) ->
    IDENTIFY_WORKLOAD(q) ->
    IDENTIFY_RESOURCE_REQUEST_AND_PLACEMENT(q) ->
    IDENTIFY_DISTRIBUTED_COMMUNICATION(q) ->
    IDENTIFY_HEALTH_PERFORMANCE_SECURITY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        cluster_topology_consistency,
        resource_inventory_evidence,
        workload_evidence,
        operational_evidence,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: GPUCluster;
    ontology: GPUClusterOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_compute_node:
        validate
        -> identify_gpu_inventory
        -> identify_memory
        -> identify_network_links
        -> add_to_cluster
        -> integrate;

    new_gpu:
        validate
        -> identify_device_type
        -> identify_memory_capacity
        -> identify_interconnect
        -> update_resource_inventory
        -> integrate;

    new_job:
        identify_resource_request
        -> identify_parallelism
        -> identify_data_requirements
        -> schedule_or_queue
        -> integrate;

    cluster_revision:
        update_nodes
        -> update_topology
        -> update_health
        -> recompute_capacity
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_cluster_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + ClusterEntity
    + ManagementEntity
    + WorkloadEntity
    + ClusterState
    + ClusterTopology
    + CollectiveCommunication
    + ClusterMonitoring
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
GPUClusterOntology
    -> DiscoverClusterResources
    -> MonitorNodeAndGPUHealth
    -> ReceiveWorkloadRequest
    -> EvaluateGPUAndMemoryRequirements
    -> SelectTopologyAwarePlacement
    -> AllocateAccelerators
    -> LaunchDistributedWorkers
    -> ExecuteParallelWorkload
    -> CoordinateCollectiveCommunication
    -> MonitorUtilisationAndFailures
    -> CheckpointAndRecoverWhereRequired
    -> ReleaseResourcesAndUpdateCapacity
    -> MaintainScalableReliableGPUCompute;

END FLOW