Context-Aware Compilation

let processDataset (data: float[]) =
    data
    |> Array.map (fun x -> complexTransform x)
    |> Array.filter (fun x -> x > threshold)
    |> Array.reduce (+)

// Pure computation - perfect for interaction nets
let matrixMultiply (a: Matrix) (b: Matrix) =
    Array2D.init a.Rows b.Cols (fun i j ->
        seq { for k in 0 .. a.Cols - 1 -> a.[i,k] * b.[k,j] }
        |> Seq.sum)

// Compiles to interaction net:
// - Every element computation is independent
// - Maps directly to GPU thread blocks
// - No synchronization needed until final result
 

// Computation with external dependencies - needs delimited continuations
let enrichDataPoints (points: DataPoint[]) = async {
    let mutable enriched = []

    for point in points do
        let! weather = WeatherService.getConditions point.Location
        let! traffic = TrafficService.getDensity point.Location

        enriched <- { point with
                        Weather = weather
                        TrafficDensity = traffic } :: enriched

    return List.rev enriched
}

// Compiles to delimited continuations:
// - Must maintain order for external service calls
// - Can optimize service call batching
// - Handles failure/retry gracefully
 

// Coeffect inference discovers the nature of computation
type ContextRequirement =
    | Pure                           // No external dependencies → Interaction nets
    | MemoryAccess of AccessPattern  // Data access pattern → Guides parallelization
    | ResourceAccess of Resource Set // External resources → Delimited continuations
    | Temporal of HistoryDepth       // Needs past values → Streaming architecture
 

// Example 1: Scientific computation
let runSimulation (grid: float[,]) =
    let nextGeneration =
        grid |> Array2D.mapi (fun i j cell ->
            let neighbors = getNeighbors grid i j
            updateCell cell neighbors)

    if converged grid nextGeneration then
        nextGeneration
    else
        runSimulation nextGeneration

// Coeffect analysis discovers:
// - Pure computation (no external resources)
// - Regular memory access pattern (stencil)
// - Recursive structure (iterative refinement)
// → Compile to interaction nets on GPU
 

// Data processing with mixed patterns
let analyzeTimeSeries (data: TimePoint[]) =
    data
    |> Array.windowed 100
    |> Array.Parallel.map (fun window ->
        let stats = computeStatistics window
        let anomalies = detectAnomalies window stats
        (stats, anomalies))
    |> consolidateResults

// Coeffect analysis:
// - Sliding window → temporal coeffect
// - Pure computation within windows
// - Window size fits in L2 cache
//
// Compilation strategy:
// - Use CPU SIMD for statistics
// - Parallelize across cores with cache-aware chunking
// - No GPU transfer overhead worth it
 

// Image processing pipeline
let processImages (images: Image[]) =
    images
    |> Array.map (fun img ->
        img
        |> applyConvolution kernel1
        |> applyConvolution kernel2
        |> normalizeHistogram)

// Coeffect analysis:
// - Pure computation
// - Regular 2D access pattern
// - High arithmetic intensity
//
// Compilation strategy:
// - Interaction nets mapping to GPU warps
// - Texture memory for convolution kernels
// - Fused kernel execution
 

// ML inference with dynamic characteristics
let runInference (model: Model) (input: Tensor) =
    let layers = model.Layers

    let rec forward (input: Tensor) (layers: Layer list) =
        match layers with
        | [] -> input
        | Linear weights :: rest ->
            // Coeffect: Matrix multiplication pattern
            let output = input |> matmul weights |> activate
            forward output rest
        | Attention heads :: rest ->
            // Coeffect: All-to-all communication pattern
            let output = multiHeadAttention input heads
            forward output rest
        | Convolution kernel :: rest ->
            // Coeffect: Local spatial pattern
            let output = convolveSpatial input kernel
            forward output rest

    forward input layers

// Coeffect analysis per layer type:
// - Linear → Pure matrix ops → Configure as systolic array
// - Attention → All-to-all → Configure as crossbar
// - Convolution → Spatial → Configure as 2D mesh
//
// Maverick reconfigures between layers
 

let monteCarloSimulation (initialState: MarketState) (scenarios: int) =
    [|1..scenarios|]
    |> Array.Parallel.map (fun i ->
        let rng = Random(i)
        simulateScenario initialState rng)
    |> Array.map calculateValue
    |> computeRiskMetrics

// Pure computation with independent scenarios
// → Interaction nets on GPU for massive parallelism
 

let processAudioStream (stream: AudioSample seq) =
    stream
    |> Seq.windowed 1024
    |> Seq.map (fun window ->
        let spectrum = FastFourierTransform window
        let features = extractFeatures spectrum
        classifySound features)

// Temporal coeffects with real-time constraints
// → CPU SIMD with predictable latency
 

let alignSequences (reference: DNA) (samples: DNA[]) =
    samples
    |> Array.Parallel.map (fun sample ->
        findBestAlignment reference sample)
    |> filterHighQualityAlignments

// Memory-intensive pure computation
// → NUMA-aware CPU parallelism
 

// The source code remains clean
let processOrder order =
    validateOrder order
    |> Result.bind calculatePricing
    |> Result.bind checkInventory
    |> Result.bind createShipment

// Compiler knows:
// - validateOrder is pure → Time CPU cycles
// - calculatePricing accesses pricing service → Track service latency
// - checkInventory queries database → Monitor query performance
// - createShipment calls external API → Record API metrics
//
// Telemetry inserted at natural boundaries with zero overhead
 

// Your AST stays clean
type PSGNode = {
    Id: NodeId
    Kind: PSGNodeKind
    Symbol: FSharpSymbol option
    SourceRange: range
}

// Coeffects live in external analysis maps
type CoeffectAnalysis = {
    ComputationPatterns: Map<NodeId, ComputationPattern>
    MemoryAccessPatterns: Map<NodeId, AccessPattern>
    ResourceRequirements: Map<NodeId, Set<Resource>>
    TemporalDependencies: Map<NodeId, HistoryDepth>
}

// Compilation uses both for intelligent decisions
let selectBackend (node: PSGNode) (coeffects: CoeffectAnalysis) =
    match Map.tryFind node.Id coeffects.ComputationPatterns with
    | Some PureDataParallel -> InteractionNetBackend
    | Some ResourceDependent -> DelimitedContinuationBackend
    | Some StreamingComputation -> PipelineParallelBackend
    | _ -> DefaultSequentialBackend

// Coeffect analysis discovers the natural CPU/GPU boundary
type HybridDeepSeekBitNet = {
    // CPU-optimized layers (ternary operations)
    BitNetAttention: {
        Operations: Add/Subtract only
        MemoryPattern: Sequential streaming
        Coeffect: Pure, LowArithmeticIntensity
        -> Target: CPU with AVX-512
    }

    // GPU-optimized components (parallel decompression)
    CompressedKVCache: {
        Operations: Parallel decompression
        MemoryPattern: Random access
        Coeffect: HighParallelism, MemoryIntensive
        -> Target: GPU with warp-level primitives
    }
}

module HybridInference =
    // Shared memory pool - no copies needed!
    let kvCache = BAREWire.createUnified<byte> {
        Size = 16<GB>
        Layout = CompressedKVLayout
        AccessPattern = CPUWrite_GPURead
    }

    // CPU processes BitNet layers efficiently
    let processBitNetLayer (input: Tensor) (weights: TernaryWeights) =
        // Just adds and subtracts - perfect for CPU!
        let output = CPU.ternaryMatmul input weights

        // Write directly to shared KV cache
        // GPU can read without any memory transfer
        kvCache.WriteCompressedScores output.AttentionScores
        output

    // GPU handles parallel KV decompression
    let retrieveContext (query: Query) =
        GPU.kernel {
            // Read directly from CPU-written memory
            let! compressed = kvCache.ReadCompressedBlock query.Range
            // Decompress in parallel across GPU warps
            let! decompressed = parallelDecompress compressed
            return reshapeToContext decompressed
        }

type QuaternaryValue =
    | Neg      // -1
    | Zero     // 0
    | Pos      // +1
    | NoValue  // Sparse/undefined

// Efficient packing enables CPU SIMD operations
type CompressedAttentionBlock = {
    Mask: uint64           // Bit mask for value presence
    PackedValues: byte[]   // 4 quaternary values per byte
    Metadata: BlockStats   // For adaptive processing
}

// CPU can efficiently process sparse patterns
let processSparseTernary (block: CompressedAttentionBlock) =
    if block.Mask = 0UL then
        Vector.zero  // Skip entirely
    else
        // SIMD process only present values
        CPU.simdTernaryOps block.PackedValues block.Mask

// Coeffects monitor and adapt the CPU/GPU split dynamically
let adaptiveHybridInference (model: HybridModel) (stream: TokenStream) =
    coeffectAsync {
        // Initial split based on static analysis
        let! initial = processTokens stream

        // Runtime telemetry through coeffects
        observe "cpu_ternary_throughput" adjustCPULoad
        observe "gpu_decompression_latency" adjustGPULoad
        observe "memory_bandwidth_usage" optimizeLayout

        // Coeffects guide dynamic rebalancing
        match workloadPattern with
        | LongContext ->
            // Shift more KV processing to GPU
            CompileStrategy.IncreaseGPUDecompression
        | RapidTokenGeneration ->
            // Maximize CPU ternary throughput
            CompileStrategy.OptimizeCPUPipeline
        | Interactive ->
            // Balance for low latency
            CompileStrategy.MinimizeHandoffs
    }