AI Runtime Ontology

FLOW ALGEBRA
AI Runtime Ontology
Kireth Semantic Research Archive

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

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "AIRuntimeOntology" v4.0

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

# ---------- PRIMARY ENTITY ----------
ENTITY AIRuntime {
    definition:
        "The software and execution environment responsible for loading,
         scheduling and executing artificial intelligence models and their
         computational operations on available hardware during training,
         evaluation or inference.";

    objectives: [
        ModelExecution,
        HardwareUtilisation,
        TensorComputation,
        MemoryManagement,
        OperatorScheduling,
        PerformanceOptimisation,
        PortableExecution
    ];
}

# ---------- CORE ENTITIES ----------
RuntimeEntity := {
    AIRuntime,
    Model,
    ModelGraph,
    Operator,
    Tensor,
    Kernel,
    ExecutionContext,
    Device
};

DeviceType := {
    CPU,
    GPU,
    AIAccelerator,
    EdgeAccelerator
};

RuntimeComponent := {
    ModelLoader,
    GraphExecutor,
    OperatorDispatcher,
    KernelLibrary,
    MemoryManager,
    DeviceManager,
    Compiler,
    Profiler
};

ExecutionMode := {
    Training,
    Evaluation,
    Inference,
    BatchInference,
    StreamingInference
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
AIRuntime LOADS Model;
Model MAY_BE_REPRESENTED_AS ModelGraph;
ModelGraph CONSISTS_OF Operator;
Operator CONSUMES Tensor;
Operator PRODUCES Tensor;
OperatorDispatcher MAPS Operator TO Kernel;
Kernel EXECUTES_ON Device;
MemoryManager ALLOCATES TensorMemory;
GraphExecutor SCHEDULES OperatorExecution;

# ---------- RUNTIME EXECUTION FLOW ----------
ModelArtifact
    -> LoadModel
    -> ValidateModel
    -> InitialiseExecutionContext
    -> SelectDevice
    -> AllocateMemory
    -> PrepareInputs
    -> ScheduleOperators
    -> DispatchKernels
    -> SynchroniseDependencies
    -> ProduceOutputs
    -> ReleaseOrReuseResources;

# ---------- MODEL LOADING ----------
ModelLoader :=
    ModelArtifact
    -> ParseRepresentation
    -> ResolveOperators
    -> ResolveParameters
    -> ConstructRuntimeModel;

ModelArtifact MAY_INCLUDE {
    Graph,
    Parameters,
    Metadata,
    PreprocessingConfiguration
};

IF UnsupportedOperatorDetected
THEN ModelLoad MAY_FAIL_OR_REQUIREFallback;

# ---------- MODEL GRAPH ----------
ModelGraph :=
    DirectedGraph(
        Operators,
        TensorDependencies
    );

DependencyEdge :=
    ProducerOperator
    -> Tensor
    -> ConsumerOperator;

GraphExecutor MUST_RESPECT DependencyOrder;

# ---------- OPERATOR ----------
Operator :=
    ComputationalTransformation;

Operator MAY_INCLUDE {
    MatrixMultiplication,
    Convolution,
    Activation,
    Normalisation,
    Attention,
    Reduction,
    ElementwiseOperation
};

Operator HAS {
    InputTensorSet,
    OutputTensorSet,
    Attributes
};

# ---------- TENSOR ----------
Tensor :=
    MultidimensionalArrayOfValues;

Tensor HAS {
    Shape,
    DataType,
    Layout,
    DeviceLocation
};

TensorShape :=
    ordered_sequence(Dimension);

# ---------- DATA TYPE ----------
TensorDataType MAY_INCLUDE {
    Float32,
    Float16,
    BFloat16,
    Int8,
    Int32,
    Boolean
};

DataType AFFECTS {
    MemoryUse,
    NumericalPrecision,
    HardwareThroughput
};

# ---------- DEVICE ----------
Device :=
    HardwareExecutionTarget;

Device HAS {
    ComputeCapability,
    MemoryCapacity,
    MemoryBandwidth
};

DeviceManager DISCOVERS AvailableDevices;

# ---------- DEVICE PLACEMENT ----------
DevicePlacement :=
    AssignOperatorOrTensor TO Device;

Placement MAY_DEPEND_ON {
    OperatorSupport,
    MemoryCapacity,
    ComputeCapability,
    DataLocality
};

# ---------- KERNEL ----------
Kernel :=
    DeviceSpecificImplementationOfOperator;

Operator MAY_HAVE MultipleKernelImplementations;

KernelSelection :=
    Operator
    + Device
    + DataType
    + Shape
    -> SelectedKernel;

# ---------- KERNEL DISPATCH ----------
OperatorDispatcher ->
    IdentifyOperator
    -> SelectKernel
    -> PrepareArguments
    -> LaunchKernel;

KernelLaunch MAY_BE {
    Synchronous,
    Asynchronous
};

# ---------- MEMORY MANAGEMENT ----------
MemoryManager :=
    Allocate
    -> Track
    -> Reuse
    -> Release;

RuntimeMemory MAY_INCLUDE {
    ModelParameterMemory,
    ActivationMemory,
    WorkspaceMemory,
    InputOutputMemory
};

# ---------- MEMORY POOL ----------
MemoryPool :=
    ReusableAllocatedMemory;

MemoryReuse MAY_REDUCE AllocationOverhead;

# ---------- ACTIVATION MEMORY ----------
Activation :=
    IntermediateTensorProducedDuringModelExecution;

Training MAY_RETAIN ActivationsForBackwardPass;

Inference MAY_RELEASE_OR_REUSE ActivationMemoryEarlier;

# ---------- PARAMETER MEMORY ----------
ModelParameter :=
    LearnedOrConfiguredTensorUsedByModel;

Parameters MAY_BE {
    LoadedIntoDeviceMemory,
    ShardedAcrossDevices,
    StreamedOrOffloaded
};

# ---------- EXECUTION CONTEXT ----------
ExecutionContext :=
    RuntimeStateForModelExecution;

ExecutionContext MAY_INCLUDE {
    DeviceState,
    MemoryState,
    StreamState,
    RuntimeOptions,
    RandomState
};

# ---------- COMPUTATION GRAPH EXECUTION ----------
GraphExecution :=
    DetermineReadyOperators
    -> DispatchReadyOperators
    -> TrackCompletion
    -> UnlockDependentOperators
    -> ContinueUntilOutputsReady;

ReadyOperator :=
    Operator WHERE RequiredInputsAvailable;

# ---------- SYNCHRONISATION ----------
Synchronisation :=
    CoordinateDependentOperationsOrDevices;

Synchronisation MAY_OCCUR_AT {
    KernelBoundary,
    DeviceBoundary,
    StreamBoundary,
    DistributedBoundary
};

ExcessiveSynchronisation MAY_REDUCE Performance;

# ---------- ASYNCHRONOUS EXECUTION ----------
AsynchronousExecution :=
    LaunchOperation
    -> ContinueHostOrIndependentWork
    -> SynchroniseWhenResultRequired;

AsyncExecution MAY_HIDE Latency;

# ---------- EXECUTION STREAM ----------
ExecutionStream :=
    OrderedQueueOfDeviceOperations;

MultipleStreams MAY_ENABLE OverlapOf {
    Compute,
    MemoryTransfer,
    IndependentKernels
};

# ---------- DATA TRANSFER ----------
TensorTransfer :=
    SourceDevice
    -> TransferData
    -> DestinationDevice;

TransferCost :=
    f(
        TensorSize,
        InterconnectBandwidth,
        TransferLatency
    );

IF TransferCostHigh
THEN PlacementEfficiency MAY_DECREASE;

# ---------- CPU EXECUTION ----------
CPUKernel MAY_USE {
    ScalarInstructions,
    SIMD,
    Multithreading,
    VectorLibraries
};

CPU MAY_EXECUTE UnsupportedAcceleratorOperationsAsFallbackWhereRuntimeAllows;

# ---------- GPU EXECUTION ----------
GPUKernel MAY_USE {
    MassiveParallelism,
    TensorCoresOrEquivalent,
    SharedMemory,
    DeviceMemory
};

GPUExecution MAY_REQUIRE HostToDeviceOrDeviceToHostTransfer;

# ---------- MULTI-DEVICE EXECUTION ----------
MultiDeviceRuntime :=
    PartitionModelOrWork
    -> PlaceAcrossDevices
    -> Execute
    -> Communicate
    -> Synchronise;

Partitioning MAY_BE {
    DataParallel,
    TensorParallel,
    PipelineParallel,
    ModelParallel
};

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

CommunicationOperation MAY_INCLUDE {
    Broadcast,
    Reduce,
    AllReduce,
    AllGather,
    Send,
    Receive
};

# ---------- TRAINING RUNTIME ----------
TrainingRuntime :=
    ForwardPass
    -> LossComputation
    -> BackwardPass
    -> GradientCommunication
    -> OptimiserStep
    -> ParameterUpdate;

TrainingRuntime MAY_REQUIRE AutomaticDifferentiation;

# ---------- AUTOMATIC DIFFERENTIATION ----------
AutomaticDifferentiation :=
    RecordOrConstructComputationDependencies
    -> ComputeDerivatives
    -> PropagateGradients;

Gradient :=
    DerivativeOfObjectiveWithRespectToParameterOrTensor;

# ---------- INFERENCE RUNTIME ----------
InferenceRuntime :=
    Input
    -> Preprocess
    -> ForwardExecution
    -> Postprocess
    -> Output;

Inference DOES_NOT_REQUIRE BackwardPassForNormalPrediction;

# ---------- BATCHING ----------
InferenceBatch :=
    MultipleInputsCombinedForSingleModelExecution;

Batching MAY_INCREASE Throughput;
LargeBatch MAY_INCREASE MemoryUseAndLatency;

# ---------- DYNAMIC BATCHING ----------
DynamicBatching :=
    QueueRequests
    -> WaitWithinConfiguredWindow
    -> FormBatch
    -> Execute
    -> SplitOutputs;

# ---------- COMPILATION ----------
RuntimeCompiler MAY_TRANSFORM ModelGraphForTargetDevice;

Compilation MAY_INCLUDE {
    OperatorFusion,
    ConstantFolding,
    LayoutTransformation,
    KernelSelection,
    MemoryPlanning
};

# ---------- OPERATOR FUSION ----------
OperatorFusion :=
    MultipleCompatibleOperators
    -> CombinedExecutionUnit;

Fusion MAY_REDUCE {
    KernelLaunchOverhead,
    IntermediateMemoryTraffic
};

# ---------- CONSTANT FOLDING ----------
ConstantFolding :=
    EvaluateConstantSubgraphAheadOfRuntimeExecution;

ConstantFolding MAY_REDUCE RuntimeWork;

# ---------- GRAPH OPTIMISATION ----------
GraphOptimisation MAY_INCLUDE {
    DeadNodeElimination,
    CommonSubexpressionOptimisation,
    OperatorFusion,
    ConstantPropagation,
    LayoutOptimisation
};

Optimisation MUST_PRESERVE RequiredModelSemanticsWithinDefinedNumericalTolerance;

# ---------- QUANTISATION ----------
QuantisedExecution :=
    UseReducedPrecisionRepresentationForSelectedOperations;

Quantisation MAY_REDUCE {
    MemoryUse,
    BandwidthUse,
    ComputeCost
};

Quantisation MAY_AFFECT ModelAccuracy;

# ---------- MIXED PRECISION ----------
MixedPrecisionExecution :=
    UseMultipleNumericPrecisionsWithinModelExecution;

MixedPrecision MAY_INCREASE AcceleratorThroughput;

NumericalStability MUST_BE Evaluated;

# ---------- LAZY EXECUTION ----------
LazyExecution :=
    DeferOperationUntilResultRequiredOrGraphCompiled;

# ---------- EAGER EXECUTION ----------
EagerExecution :=
    ExecuteOperationsAsProgramInvokesThem;

Runtime MAY_SUPPORT {
    EagerMode,
    GraphMode,
    CompiledMode
};

# ---------- PROFILING ----------
RuntimeProfiler MAY_MEASURE {
    OperatorLatency,
    KernelLatency,
    MemoryUsage,
    DeviceUtilisation,
    DataTransfer,
    SynchronisationTime
};

Profiling IDENTIFIES RuntimeBottleneck;

# ---------- PERFORMANCE ----------
RuntimePerformance :=
    f(
        ComputeThroughput,
        MemoryBandwidth,
        KernelEfficiency,
        TransferCost,
        Synchronisation,
        BatchSize
    );

# ---------- LATENCY ----------
InferenceLatency :=
    OutputReadyTime - InputArrivalTime;

Latency MAY_INCLUDE {
    QueueTime,
    Preprocessing,
    RuntimeExecution,
    Postprocessing
};

# ---------- THROUGHPUT ----------
InferenceThroughput :=
    InputsProcessed / Time;

# ---------- WARMUP ----------
RuntimeWarmup :=
    InitialExecutionsUsedToPrepareCachesCompilationOrDeviceState;

Warmup MAY_CAUSE InitialLatencyHigherThanSteadyStateLatency;

# ---------- MODEL CACHE ----------
Runtime MAY_CACHE {
    CompiledGraph,
    KernelSelection,
    MemoryPlan,
    ModelParameters
};

Caching MAY_REDUCE RepeatedSetupCost;

# ---------- ERROR ----------
RuntimeError MAY_INCLUDE {
    UnsupportedOperator,
    InvalidShape,
    InvalidDataType,
    OutOfMemory,
    DeviceFailure,
    KernelFailure,
    NumericalFailure
};

# ---------- OUT OF MEMORY ----------
IF RequiredMemory > AvailableMemory
THEN OutOfMemory := true;

Mitigation MAY_INCLUDE {
    ReduceBatchSize,
    FreeCache,
    Offload,
    ShardModel,
    UseLowerPrecision
};

# ---------- FALLBACK ----------
Fallback :=
    ExecuteOperationOnAlternativeSupportedBackend;

Fallback MAY_PRESERVE Functionality
BUT MAY_REDUCE Performance;

# ---------- PORTABILITY ----------
RuntimePortability :=
    AbilityToExecuteModelAcrossDifferentHardwareOrOperatingEnvironments;

Portability MAY_REQUIRE {
    StandardModelFormat,
    BackendAbstraction,
    PortableOperators
};

# ---------- MODEL FORMAT ----------
ModelFormat MAY_BE {
    FrameworkNativeFormat,
    InterchangeFormat,
    CompiledRuntimeFormat
};

Runtime MUST_SUPPORT RequiredFormatOrConversion;

# ---------- SECURITY ----------
AIRuntimeSecurity MAY_INCLUDE {
    ModelIntegrity,
    InputValidation,
    MemoryIsolation,
    DependencyIntegrity,
    DeviceAccessControl
};

UntrustedModelArtifact MAY_CREATE SecurityRisk;

# ---------- OBSERVABILITY ----------
RuntimeObservability MAY_INCLUDE {
    ExecutionMetrics,
    DeviceMetrics,
    Logs,
    Traces,
    Errors
};

RuntimeTrace MAY_CONNECT OperatorExecutionToDeviceKernel;

# ---------- SEMANTIC REASONING ----------
IF SoftwareLoadsAndExecutesAIModelOperationsOnHardware
THEN AIRuntimeClassification := supported;

IF OperatorUnsupportedOnSelectedDevice
THEN AlternativeKernelOrFallback MAY_BE_REQUIRED;

IF TensorMemoryExceedsDeviceCapacity
THEN OutOfMemoryRisk := high;

IF OperatorFusionReducesIntermediateTransfers
THEN RuntimeEfficiency MAY_INCREASE;

IF BatchSizeIncreases
THEN Throughput MAY_INCREASE
AND MemoryUse MAY_INCREASE;

IF DeviceTransferDominatesExecution
THEN Placement SHOULD_BE Reconsidered;

IF RuntimeUsesReducedPrecision
THEN NumericalAndModelQuality SHOULD_BE Validated;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "AI runtime" ~= "artificial intelligence runtime";
    "ML runtime" ~= "machine learning runtime";
    "inference runtime" ~= "model inference execution runtime";
    "runtime engine" ~= "AI execution engine" WHEN context == model_execution;
    "operator kernel" ~= "device kernel" WHEN context == AI_runtime;
    "graph executor" ~= "model graph execution engine";
    "device placement" ~= "operator placement" WHEN context == AI_execution;
    "model runtime" ~= "model execution environment";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_MODEL_AND_GRAPH(q) ->
    IDENTIFY_OPERATORS_AND_TENSORS(q) ->
    IDENTIFY_DEVICE(q) ->
    IDENTIFY_KERNELS(q) ->
    IDENTIFY_MEMORY_REQUIREMENTS(q) ->
    IDENTIFY_EXECUTION_MODE(q) ->
    IDENTIFY_OPTIMISATION_AND_PERFORMANCE(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        runtime_semantic_consistency,
        device_compatibility,
        execution_evidence,
        performance_evidence,
        contextual_relevance
    );

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

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: AIRuntime;
    ontology: AIRuntimeOntology;
    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_model:
        validate_format
        -> parse_graph
        -> identify_operators
        -> identify_parameters
        -> integrate;

    new_device:
        validate
        -> identify_compute_capability
        -> identify_memory
        -> identify_supported_kernels
        -> integrate;

    new_kernel:
        identify_operator
        -> identify_device
        -> identify_data_types
        -> identify_performance
        -> integrate;

    runtime_revision:
        update_graph_optimisations
        -> update_kernel_selection
        -> update_memory_plan
        -> recompute_performance
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + RuntimeEntity
    + DeviceType
    + RuntimeComponent
    + ExecutionMode
    + ModelGraph
    + MemoryManager
    + RuntimePerformance
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
AIRuntimeOntology
    -> LoadModelArtifact
    -> ConstructRuntimeModel
    -> IdentifyOperatorsAndDependencies
    -> SelectExecutionDevices
    -> PlanTensorMemory
    -> SelectAndDispatchKernels
    -> ExecuteModelGraph
    -> SynchroniseDependencies
    -> TransferDataWhereRequired
    -> ProduceModelOutputs
    -> ProfileRuntimePerformance
    -> OptimiseExecutionAndMemory
    -> MaintainReliablePortableAIExecution;

END FLOW