Supercomputer Ontology

FLOW ALGEBRA
Supercomputer Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/high-performance-computing/supercomputer-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "SupercomputerOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := HighPerformanceComputing
ONTOLOGY := SupercomputerOntology

# ---------- PRIMARY ENTITY ----------
ENTITY Supercomputer {
    definition:
        "A large-scale high-performance computing system composed of many
         tightly coordinated processing, memory, storage and networking
         resources designed to execute extremely demanding computational
         workloads at very high speed and scale.";

    objectives: [
        ExtremePerformance,
        MassiveParallelism,
        LargeScaleSimulation,
        HighThroughput,
        Scalability,
        ScientificComputation
    ];
}

# ---------- CORE ENTITIES ----------
SupercomputerSystem := {
    ComputeNode,
    Processor,
    Accelerator,
    Memory,
    Interconnect,
    StorageSystem,
    Scheduler,
    ManagementSystem
};

ComputeNode := {
    CPUNode,
    GPUNode,
    AcceleratorNode,
    LoginNode,
    ServiceNode
};

ComputeResource := {
    CPUCore,
    GPU,
    Accelerator,
    MemoryCapacity,
    NetworkBandwidth,
    StorageBandwidth
};

Workload := {
    HPCJob,
    ParallelApplication,
    Simulation,
    ScientificModel,
    DataIntensiveApplication
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Supercomputer CONSISTS_OF ComputeNode;
ComputeNode CONTAINS Processor;
ComputeNode MAY_CONTAIN Accelerator;
ComputeNode HAS Memory;
ComputeNode CONNECTS_THROUGH HighSpeedInterconnect;
Supercomputer USES ParallelStorage;
Scheduler ALLOCATES ComputeResource TO Workload;
Workload EXECUTES_ON Supercomputer;

# ---------- SYSTEM ARCHITECTURE ----------
SupercomputerArchitecture :=
    ComputeSubsystem
    + MemorySubsystem
    + InterconnectSubsystem
    + StorageSubsystem
    + ManagementSubsystem;

ComputeSubsystem PROVIDES ProcessingCapability;
InterconnectSubsystem PROVIDES NodeCommunication;
StorageSubsystem PROVIDES PersistentDataAccess;
ManagementSubsystem CONTROLS SystemOperation;

# ---------- COMPUTE NODE ----------
ComputeNode :=
    {
        ProcessorSet,
        AcceleratorSet,
        LocalMemory,
        NetworkInterface,
        LocalStorage
    };

NodeState := {
    Available,
    Allocated,
    Busy,
    Draining,
    Maintenance,
    Failed
};

# ---------- MASSIVE PARALLELISM ----------
MassiveParallelism :=
    LargeNumberOfProcessingElements
    EXECUTE ConcurrentWork;

ParallelApplication ->
    DecomposeProblem
    -> MapWorkAcrossNodes
    -> ExecuteConcurrently
    -> Communicate
    -> Synchronise
    -> AggregateResult;

# ---------- PROCESSOR ----------
Processor MAY_BE {
    GeneralPurposeCPU,
    VectorProcessor,
    ManyCoreProcessor
};

Processor EXECUTES Instructions;
Processor CONTAINS ProcessingCore;

# ---------- ACCELERATOR ----------
Accelerator MAY_BE {
    GPU,
    SpecialisedProcessor,
    MatrixAccelerator
};

Accelerator EXECUTES SuitableParallelKernel;

HeterogeneousNode :=
    CPU
    + Accelerator;

# ---------- MEMORY ----------
SupercomputerMemory :=
    DistributedAcrossComputeNodes;

NodeMemory LOCAL_TO ComputeNode;

MemoryHierarchy MAY_INCLUDE {
    Register,
    Cache,
    MainMemory,
    AcceleratorMemory,
    Storage
};

DataLocality AFFECTS Performance;

# ---------- INTERCONNECT ----------
HighSpeedInterconnect CONNECTS ComputeNodes;

InterconnectProperty := {
    Bandwidth,
    Latency,
    Topology,
    Reliability
};

CommunicationPerformance :=
    f(
        MessageSize,
        MessageCount,
        Latency,
        Bandwidth,
        Topology
    );

IF CommunicationCostHigh
THEN ParallelEfficiency := DECREASE;

# ---------- NETWORK TOPOLOGY ----------
InterconnectTopology MAY_BE {
    Mesh,
    Torus,
    FatTree,
    Dragonfly,
    HierarchicalNetwork
};

Topology AFFECTS {
    Routing,
    Contention,
    CommunicationDistance,
    Scalability
};

# ---------- PARALLEL STORAGE ----------
ParallelStorage :=
    StorageSystemSupportingConcurrentAccessByManyNodes;

ParallelFileSystem MAY_BE_USED_BY Supercomputer;

IOWorkload :=
    ReadOperations
    + WriteOperations
    + MetadataOperations;

IOPerformance :=
    f(
        DataVolume,
        Concurrency,
        AccessPattern,
        StorageBandwidth
    );

# ---------- JOB SCHEDULING ----------
HPCJob :=
    {
        JobIdentifier,
        Executable,
        ResourceRequest,
        InputData,
        RuntimeLimit,
        Priority,
        JobState
    };

JobState := {
    Submitted,
    Queued,
    Scheduled,
    Running,
    Completed,
    Failed,
    Cancelled
};

Scheduler ->
    ReceiveJob
    -> EvaluateResourceRequest
    -> Queue
    -> SelectResources
    -> AllocateNodes
    -> LaunchJob;

# ---------- RESOURCE REQUEST ----------
ResourceRequest MAY_INCLUDE {
    NodeCount,
    CPUCount,
    GPUCount,
    MemoryAmount,
    RuntimeLimit,
    StorageRequirement
};

IF RequestedResourcesAvailable
THEN Job MAY_BE Scheduled;

# ---------- PARALLEL EXECUTION ----------
ParallelExecutionModel MAY_INCLUDE {
    MessagePassing,
    SharedMemory,
    HybridParallelism,
    AcceleratorParallelism
};

MessagePassing COORDINATES ProcessesAcrossNodes;
SharedMemory COORDINATES ThreadsWithinNode;

# ---------- PERFORMANCE ----------
SupercomputerPerformance MAY_BE_MEASURED_BY {
    FloatingPointOperationsPerSecond,
    ApplicationRuntime,
    Throughput,
    ParallelEfficiency,
    MemoryBandwidth,
    IOThroughput
};

FLOPS :=
    FloatingPointOperations / Second;

PeakPerformance :=
    TheoreticalMaximumComputeRate;

SustainedPerformance :=
    ObservedPerformanceOnWorkload;

SustainedPerformance <= PeakPerformance;

# ---------- SPEEDUP ----------
Speedup(P) :=
    SerialRuntime
    / ParallelRuntime(P);

Efficiency(P) :=
    Speedup(P) / P;

IF EfficiencyDecreasesWithScale
THEN ScalingOverhead := INCREASE;

# ---------- SCALABILITY ----------
StrongScaling :=
    FixedProblemSize
    + IncreasingResources;

WeakScaling :=
    ProblemSizeIncreasesWithResources;

Supercomputer SHOULD_SUPPORT LargeResourceCounts;

# ---------- LOAD BALANCE ----------
LoadBalance :=
    SimilarUsefulWorkAcrossAllocatedResources;

IF SomeNodesIdleWhileOthersRemainBusy
THEN LoadImbalance := true;

LoadImbalance REDUCES ResourceEfficiency;

# ---------- CHECKPOINTING ----------
Checkpoint :=
    SavedStateOfLongRunningComputation;

Checkpointing MAY_ENABLE {
    FailureRecovery,
    JobRestart
};

IF JobFails
AND ValidCheckpointExists
THEN Job MAY_RESTART_FROM Checkpoint;

# ---------- FAULT MODEL ----------
SupercomputerFailure := {
    ComputeNodeFailure,
    ProcessorFailure,
    AcceleratorFailure,
    MemoryFailure,
    InterconnectFailure,
    StorageFailure,
    PowerFailure
};

LargeComponentCount MAY_INCREASE FrequencyOfComponentFailure;

Resilience MAY_USE {
    CheckpointRestart,
    Redundancy,
    FaultDetection,
    TaskRecovery,
    JobResubmission
};

# ---------- COOLING ----------
CoolingSystem REMOVES HeatGeneratedBy ComputeInfrastructure;

ThermalState AFFECTS HardwareReliabilityAndPerformance;

IF TemperatureExceedsSafeThreshold
THEN {
    Performance MAY_BE Throttled;
    HardwareRisk := INCREASE;
};

# ---------- POWER ----------
PowerSystem SUPPLIES Supercomputer;

EnergyConsumption :=
    Power * Time;

EnergyEfficiency :=
    UsefulComputation / EnergyConsumption;

PerformancePerWatt :=
    ComputationalPerformance / Power;

# ---------- FACILITY ----------
SupercomputerFacility MAY_INCLUDE {
    PowerDistribution,
    CoolingInfrastructure,
    NetworkInfrastructure,
    PhysicalSecurity
};

FacilityInfrastructure SUPPORTS SupercomputerOperation;

# ---------- SCIENTIFIC APPLICATIONS ----------
Supercomputer MAY_EXECUTE {
    ClimateSimulation,
    WeatherPrediction,
    AstrophysicalSimulation,
    MolecularSimulation,
    ComputationalFluidDynamics,
    EngineeringSimulation,
    QuantumSystemSimulation,
    LargeScaleAIWorkload
};

ScientificProblem ->
    MathematicalModel
    -> NumericalAlgorithm
    -> ParallelImplementation
    -> SupercomputerExecution
    -> ScientificResult;

# ---------- OBSERVABILITY ----------
SupercomputerObservability := {
    NodeMetrics,
    JobMetrics,
    PowerMetrics,
    ThermalMetrics,
    NetworkMetrics,
    StorageMetrics,
    Logs
};

MonitoringSystem OBSERVES SupercomputerSystem;

# ---------- SECURITY ----------
SupercomputerSecurity MAY_INCLUDE {
    UserAuthentication,
    Authorisation,
    NetworkSegmentation,
    DataProtection,
    JobIsolation,
    SoftwareIntegrity
};

User SUBMITS Job;
Authorisation DETERMINES AccessibleResources;

# ---------- SEMANTIC REASONING ----------
IF ComputingSystemHasMassiveParallelResources
AND HighSpeedInterconnect
AND DesignedForExtremeComputationalPerformance
THEN SupercomputerClassification := supported;

IF CommunicationCostIncreases
THEN ParallelEfficiency MAY_DECREASE;

IF WorkloadUsesAcceleratorsEffectively
THEN PerformancePotential := INCREASE;

IF JobRequiresMoreResourcesThanCurrentlyAvailable
THEN JobState := Queued;

IF ComponentFailureOccurs
AND ValidCheckpointExists
THEN RecoveryPotential := INCREASE;

IF CoolingInsufficient
THEN ThermalRisk := INCREASE;

IF SustainedPerformanceNearPeakPerformance
THEN ComputationalEfficiencyEvidence := HIGH;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "supercomputer" ~= "supercomputing system";
    "supercomputing" ~= "large-scale high performance computing";
    "compute node" ~= "supercomputer compute node";
    "high-speed interconnect" ~= "HPC interconnect";
    "parallel file system" ~= "high-performance parallel storage";
    "peak performance" ~= "theoretical peak compute performance";
    "sustained performance" ~= "observed application performance";
    "performance per watt" ~= "computational energy efficiency";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_SUPERCOMPUTER_SYSTEM(q) ->
    IDENTIFY_COMPUTE_NODES(q) ->
    IDENTIFY_PROCESSORS_ACCELERATORS(q) ->
    IDENTIFY_INTERCONNECT(q) ->
    IDENTIFY_STORAGE(q) ->
    IDENTIFY_WORKLOAD(q) ->
    IDENTIFY_PERFORMANCE(q) ->
    IDENTIFY_RESILIENCE(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        architecture_consistency,
        performance_consistency,
        semantic_consistency,
        evidence_strength,
        contextual_relevance
    );

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

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: Supercomputer;
    ontology: SupercomputerOntology;
    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_processors
        -> identify_accelerators
        -> identify_memory
        -> identify_interconnect
        -> integrate;

    new_workload:
        classify
        -> identify_resource_requirements
        -> identify_parallel_model
        -> identify_storage_requirements
        -> integrate;

    new_performance_evidence:
        update_runtime
        -> update_throughput
        -> recompute_efficiency
        -> identify_bottleneck
        -> integrate;

    system_revision:
        update_topology
        -> update_resource_inventory
        -> update_failure_state
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + SupercomputerSystem
    + ComputeNode
    + ComputeResource
    + Workload
    + InterconnectTopology
    + SupercomputerPerformance
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
SupercomputerOntology
    -> IdentifyComputationalWorkload
    -> DetermineResourceRequirements
    -> ScheduleHPCJob
    -> AllocateComputeNodes
    -> MapParallelWork
    -> ExecuteAcrossProcessorsAndAccelerators
    -> CoordinateThroughHighSpeedInterconnect
    -> AccessParallelStorage
    -> MonitorPerformanceAndHealth
    -> RecoverFromComponentFailure
    -> AnalyseScalingAndEfficiency
    -> ProduceLargeScaleComputationalResult;

END FLOW