GPU Computing Ontology

FLOW ALGEBRA
GPU Computing Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/high-performance-computing/gpu-computing-ontology-3.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "GPUComputingOntology" v4.0

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

# ---------- PRIMARY ENTITY ----------
ENTITY GPUComputing {
    definition:
        "A parallel computing model that uses graphics processing units and
         their highly parallel execution architecture to accelerate workloads
         containing large amounts of data-parallel or throughput-oriented
         computation.";

    objectives: [
        MassiveParallelism,
        HighThroughput,
        Acceleration,
        DataParallelExecution,
        ComputationalEfficiency
    ];
}

# ---------- CORE ENTITIES ----------
GPUEntity := {
    GPU,
    ComputeUnit,
    StreamingProcessor,
    Thread,
    ThreadGroup,
    Kernel,
    HostProcessor
};

GPUMemory := {
    RegisterMemory,
    LocalOrSharedMemory,
    Cache,
    GlobalMemory,
    ConstantMemory,
    HostMemory
};

GPUWorkload := {
    Kernel,
    DataParallelTask,
    NumericalComputation,
    Simulation,
    MatrixOperation,
    MachineLearningWorkload
};

ExecutionProperty := {
    Parallelism,
    Occupancy,
    Throughput,
    MemoryBandwidth,
    Latency,
    Synchronisation
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
HostProcessor CONTROLS_OR_LAUNCHES GPUWorkload;
GPU EXECUTES Kernel;
Kernel CONSISTS_OF ParallelThreads;
Thread EXECUTES Instructions;
ThreadGroup GROUPS Threads;
ComputeUnit SCHEDULES ThreadGroup;
GPU ACCESSES GPUMemory;
GPU MAY_TRANSFER_DATA_WITH HostMemory;

# ---------- HOST-DEVICE MODEL ----------
Host ->
    PrepareData
    -> AllocateDeviceMemory
    -> TransferInput
    -> LaunchKernel
    -> ExecuteOnGPU
    -> Synchronise
    -> TransferResult
    -> ContinueHostExecution;

HostProcessor AND GPU ARE DistinctComputeDomainsInCommonAcceleratorModel;

# ---------- GPU KERNEL ----------
Kernel :=
    FunctionExecutedByManyParallelThreads;

KernelLaunch :=
    {
        KernelFunction,
        GridDimensions,
        GroupDimensions,
        Arguments
    };

KernelLaunch CREATES ParallelExecutionConfiguration;

# ---------- THREAD HIERARCHY ----------
ExecutionHierarchy :=
    Grid
    -> ThreadGroup
    -> Thread;

Grid CONTAINS ThreadGroup;
ThreadGroup CONTAINS Thread;

Thread HAS {
    ThreadIdentifier,
    LocalIndex,
    GlobalIndex
};

Thread MAY_PROCESS DataElement;

# ---------- DATA PARALLELISM ----------
DataParallelGPUExecution :=
    PartitionData
    -> MapElementsToThreads
    -> ExecuteKernel
    -> CombineOrStoreResults;

IF SameOperationAppliedAcrossManyDataElements
THEN GPUSuitability := INCREASE;

# ---------- SIMD / SIMT RELATIONSHIP ----------
GPUExecution MAY_USE SIMTStyleExecution;

SIMT :=
    SingleInstructionMultipleThreads;

ThreadsGroupedForExecution MAY_SHARE InstructionFlow;

IF ThreadsDivergeOnControlFlow
THEN ExecutionEfficiency MAY_DECREASE;

# ---------- WARP / WAVEFRONT CONCEPT ----------
ExecutionGroup :=
    HardwareScheduledGroupOfThreads;

ExecutionGroup MAY_BE_CALLED {
    Warp,
    Wavefront,
    Subgroup
};

BranchDivergence WITHIN ExecutionGroup MAY_SERIALISE Paths;

# ---------- GPU MEMORY HIERARCHY ----------
MemoryHierarchy :=
    RegisterMemory
    -> SharedOrLocalMemory
    -> Cache
    -> GlobalMemory
    -> HostMemory;

MemoryAccessCost VARIES_BY MemoryLevel;

DataLocality AND AccessPattern AFFECT Performance;

# ---------- GLOBAL MEMORY ----------
GlobalMemory :=
    LargeDeviceMemoryAccessibleByGPUThreads;

GlobalMemory HAS {
    HighBandwidth,
    HigherLatencyThanRegistersOrSharedMemory
};

CoalescedAccess MAY_IMPROVE EffectiveMemoryBandwidth;

# ---------- SHARED / LOCAL MEMORY ----------
SharedMemory :=
    FastMemorySharedWithinThreadGroup;

SharedMemory MAY_SUPPORT {
    DataReuse,
    InterThreadCooperation,
    ReducedGlobalMemoryTraffic
};

Synchronisation MAY_BE_REQUIRED_AFTER SharedMemoryWrites;

# ---------- REGISTER MEMORY ----------
RegisterMemory :=
    FastPerThreadStorage;

HighRegisterUsage MAY_REDUCE NumberOfConcurrentResidentThreads;

# ---------- MEMORY TRANSFER ----------
HostToDeviceTransfer :=
    Copy(HostMemory, GPUMemory);

DeviceToHostTransfer :=
    Copy(GPUMemory, HostMemory);

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

IF TransferCostDominatesKernelCompute
THEN AccelerationBenefit := DECREASE;

# ---------- UNIFIED / SHARED ADDRESSING ----------
UnifiedMemoryModel MAY_ALLOW ManagedDataMovementBetweenHostAndGPU;

UnifiedAddressing MAY_SIMPLIFY MemoryAccessModel;

PhysicalDataMovement MAY_STILL_OCCUR;

# ---------- OCCUPANCY ----------
Occupancy :=
    ActiveExecutionGroups
    / MaximumSupportedExecutionGroups;

Occupancy AFFECTED_BY {
    RegisterUsage,
    SharedMemoryUsage,
    ThreadGroupSize,
    HardwareLimits
};

HighOccupancy MAY_HELP_HIDE Latency;
MaximumOccupancy DOES_NOT_ALWAYS_IMPLY MaximumPerformance;

# ---------- LATENCY HIDING ----------
GPU MAY_HIDE MemoryOrInstructionLatency
BY SchedulingOtherReadyExecutionGroups;

IF SufficientParallelWorkAvailable
THEN LatencyHidingPotential := INCREASE;

# ---------- SYNCHRONISATION ----------
ThreadGroupBarrier :=
    SynchroniseThreadsWithinGroup;

GlobalSynchronisation MAY_REQUIRE {
    KernelBoundary,
    SpecialisedMechanism
};

ExcessiveSynchronisation MAY_REDUCE Throughput;

# ---------- ATOMIC OPERATION ----------
AtomicOperation :=
    IndivisibleUpdateToSharedMemoryLocation;

AtomicOperation PRESERVES CorrectnessForCertainConcurrentUpdates;

HighAtomicContention MAY_REDUCE Performance;

# ---------- COMPUTE INTENSITY ----------
ArithmeticIntensity :=
    ArithmeticOperations / DataMovement;

IF ArithmeticIntensityHigh
THEN ComputeBoundBehaviour MAY_INCREASE;

IF ArithmeticIntensityLow
THEN MemoryBandwidth MAY_DOMINATE;

# ---------- PERFORMANCE ----------
KernelExecutionTime :=
    EndTime - StartTime;

GPUThroughput :=
    WorkCompleted / Time;

AccelerationSpeedup :=
    CPUExecutionTime
    / GPUExecutionTime;

OverallApplicationSpeedup MUST_INCLUDE {
    DataTransfer,
    KernelLaunchOverhead,
    CPUWork,
    GPUWork
};

# ---------- ROOFLINE RELATIONSHIP ----------
AttainablePerformance :=
    min(
        PeakComputePerformance,
        MemoryBandwidth * ArithmeticIntensity
    );

IF ArithmeticIntensityBelowBalancePoint
THEN Workload MAY_BE MemoryBound;

# ---------- MULTI-GPU ----------
MultiGPUComputing :=
    DistributeWorkAcross MultipleGPUs;

MultiGPUCommunication MAY_USE {
    HostMediatedTransfer,
    DeviceInterconnect,
    NetworkCommunication
};

MultiGPUScaling DEPENDS_ON {
    WorkPartitioning,
    CommunicationCost,
    LoadBalance
};

# ---------- GPU CLUSTER ----------
GPUCluster :=
    MultipleComputeNodes
    WITH OneOrMoreGPUsPerNode;

GPUCluster SUPPORTS DistributedAcceleratedComputing;

InterNodeCommunication MAY_COORDINATE MultiGPUWorkload;

# ---------- GPU APPLICATIONS ----------
GPUComputing MAY_ACCELERATE {
    LinearAlgebra,
    ScientificSimulation,
    ImageProcessing,
    SignalProcessing,
    MachineLearning,
    DeepLearning,
    ComputationalPhysics,
    MolecularSimulation
};

# ---------- FAILURE ----------
GPUFailure := {
    KernelFailure,
    MemoryAllocationFailure,
    DeviceFailure,
    TransferFailure,
    ResourceExhaustion
};

IF KernelExecutionFails
THEN ResultValidity := false;

# ---------- ENERGY ----------
GPUPerformancePerWatt :=
    UsefulGPUPerformance / PowerConsumption;

GPUAcceleration MAY_IMPROVE EnergyEfficiencyForSuitableWorkloads;

# ---------- SEMANTIC REASONING ----------
IF WorkloadContainsLargeIndependentDataParallelOperations
THEN GPUSuitability := HIGH;

IF BranchDivergenceHigh
THEN ExecutionEfficiency := DECREASE;

IF MemoryAccessUncoalesced
THEN EffectiveMemoryBandwidth MAY_DECREASE;

IF HostDeviceTransferLargeRelativeToKernelWork
THEN OverallSpeedup := DECREASE;

IF SharedMemoryEnablesDataReuse
THEN GlobalMemoryTraffic := DECREASE;

IF RegisterUsageTooHigh
THEN Occupancy MAY_DECREASE;

IF ArithmeticIntensityHigh
AND ParallelismHigh
THEN ComputeThroughputPotential := INCREASE;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "GPU computing" ~= "graphics processor computing";
    "GPGPU" ~= "general-purpose GPU computing";
    "GPU kernel" ~= "device kernel" WHEN context == gpu_computing;
    "thread block" ~= "thread group";
    "work group" ~= "thread group" WHEN context == parallel_accelerator;
    "warp" ~= "hardware execution group" WHEN context == GPU;
    "device memory" ~= "GPU memory";
    "host-device transfer" ~= "CPU-GPU data transfer";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_GPU_ENTITY(q) ->
    IDENTIFY_KERNEL(q) ->
    IDENTIFY_THREAD_HIERARCHY(q) ->
    IDENTIFY_MEMORY_LEVEL(q) ->
    IDENTIFY_DATA_TRANSFER(q) ->
    IDENTIFY_PERFORMANCE_PROPERTY(q) ->
    IDENTIFY_PARALLELISM(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        gpu_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: GPUComputing;
    ontology: GPUComputingOntology;
    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_gpu_entity:
        validate
        -> classify
        -> identify_execution_role
        -> relate
        -> integrate;

    new_kernel:
        identify_thread_structure
        -> identify_memory_access
        -> identify_synchronisation
        -> identify_resource_usage
        -> integrate;

    new_performance_evidence:
        update_kernel_time
        -> update_transfer_time
        -> recompute_speedup
        -> identify_bottleneck
        -> integrate;

    architecture_revision:
        update_compute_resources
        -> update_memory_model
        -> update_execution_limits
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + GPUEntity
    + GPUMemory
    + GPUWorkload
    + ExecutionProperty
    + ExecutionHierarchy
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
GPUComputingOntology
    -> IdentifyParallelWorkload
    -> PrepareHostData
    -> AllocateGPUMemory
    -> ConfigureKernelLaunch
    -> MapDataToThreads
    -> ExecuteGPUKernel
    -> CoordinateThreads
    -> OptimiseMemoryAccess
    -> SynchroniseExecution
    -> TransferOrConsumeResults
    -> MeasureGPUPerformance
    -> OptimiseParallelThroughput
    -> ProduceAcceleratedResult;

END FLOW