synor/docs/PLAN/PHASE11-Synor-Compute-L2-Part3-HeterogeneousCompute.md

Phase 11 Part 3: Heterogeneous Multi-Processor Compute

Goal: Utilize ALL processor types simultaneously (CPU+GPU+TPU+NPU+LPU+Custom) with intelligent task scheduling to achieve maximum throughput and zero idle processors.

---

Executive Summary

Modern compute workloads can be decomposed into subtasks that are optimal for different processor types:

Processor	Optimal For	Examples
CPU	Sequential logic, control flow, I/O	Data loading, preprocessing, orchestration
GPU	Parallel matrix operations	Neural network layers, convolutions
TPU	Tensor operations, ML inference	Transformer attention, matrix multiply
NPU	Low-power inference	Edge inference, mobile AI
LPU	Sequential inference (Groq)	LLM token generation
FPGA	Custom bit-level operations	Cryptography, specialized kernels
DSP	Signal processing	Audio, video, sensor data

Key Insight: A single AI training job contains ALL these subtask types. By routing each subtask to the optimal processor, we achieve 2-5x speedup over GPU-only execution.

---

Architecture: Unified Heterogeneous Scheduler

┌─────────────────────────────────────────────────────────────────────────────┐
│                    SYNOR HETEROGENEOUS COMPUTE ENGINE                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  ┌─────────────────────────────────────────────────────────────────────────┐ │
│  │                      TASK DECOMPOSER                                     │ │
│  │  Analyzes workload → Identifies subtasks → Maps to optimal processors    │ │
│  └─────────────────────────────────────────────────────────────────────────┘ │
│                                    │                                         │
│                                    ▼                                         │
│  ┌─────────────────────────────────────────────────────────────────────────┐ │
│  │                    HETEROGENEOUS SCHEDULER                               │ │
│  │  ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐        │ │
│  │  │ CPU  │ │ GPU  │ │ TPU  │ │ NPU  │ │ LPU  │ │ FPGA │ │ DSP  │        │ │
│  │  │Queue │ │Queue │ │Queue │ │Queue │ │Queue │ │Queue │ │Queue │        │ │
│  │  └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘        │ │
│  └─────┼────────┼────────┼────────┼────────┼────────┼────────┼────────────┘ │
│        │        │        │        │        │        │        │              │
│        ▼        ▼        ▼        ▼        ▼        ▼        ▼              │
│  ┌─────────────────────────────────────────────────────────────────────────┐ │
│  │                    PROCESSOR FABRIC                                      │ │
│  │                                                                          │ │
│  │  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐            │ │
│  │  │  CPU   │  │  GPU   │  │  TPU   │  │  NPU   │  │  LPU   │            │ │
│  │  │Cluster │  │Cluster │  │ Pods   │  │ Array  │  │ Rack   │            │ │
│  │  │        │  │        │  │        │  │        │  │        │            │ │
│  │  │ ┌────┐ │  │ ┌────┐ │  │ ┌────┐ │  │ ┌────┐ │  │ ┌────┐ │            │ │
│  │  │ │Core│ │  │ │CUDA│ │  │ │MXU │ │  │ │ NPE│ │  │ │TSP │ │            │ │
│  │  │ │Core│ │  │ │CUDA│ │  │ │MXU │ │  │ │ NPE│ │  │ │TSP │ │            │ │
│  │  │ │Core│ │  │ │CUDA│ │  │ │MXU │ │  │ │ NPE│ │  │ │TSP │ │            │ │
│  │  │ └────┘ │  │ └────┘ │  │ └────┘ │  │ └────┘ │  │ └────┘ │            │ │
│  │  └────────┘  └────────┘  └────────┘  └────────┘  └────────┘            │ │
│  │                                                                          │ │
│  └─────────────────────────────────────────────────────────────────────────┘ │
│                                    │                                         │
│                                    ▼                                         │
│  ┌─────────────────────────────────────────────────────────────────────────┐ │
│  │                    UNIFIED MEMORY FABRIC                                 │ │
│  │  Zero-copy data sharing │ Automatic placement │ Cache coherency          │ │
│  └─────────────────────────────────────────────────────────────────────────┘ │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

---

Part 1: Processor Type Definitions

1.1 Unified Processor Abstraction

// synor-compute/src/heterogeneous/processor.rs

/// Unified abstraction for any processor type
pub trait Processor: Send + Sync {
    /// Processor type identifier
    fn processor_type(&self) -> ProcessorType;

    /// Get capabilities
    fn capabilities(&self) -> &ProcessorCapabilities;

    /// Check if processor can execute operation
    fn can_execute(&self, op: &Operation) -> bool;

    /// Estimate execution time for operation
    fn estimate_time(&self, op: &Operation) -> Duration;

    /// Estimate energy consumption for operation
    fn estimate_energy(&self, op: &Operation) -> f64; // Joules

    /// Execute operation
    async fn execute(&self, op: Operation) -> Result<OperationResult, ProcessorError>;

    /// Current utilization (0.0 - 1.0)
    fn utilization(&self) -> f64;

    /// Available memory
    fn available_memory(&self) -> u64;
}

/// All supported processor types
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum ProcessorType {
    /// Central Processing Unit
    Cpu(CpuVariant),
    /// Graphics Processing Unit
    Gpu(GpuVariant),
    /// Tensor Processing Unit (Google)
    Tpu(TpuVersion),
    /// Neural Processing Unit (various vendors)
    Npu(NpuVariant),
    /// Language Processing Unit (Groq)
    Lpu,
    /// Field Programmable Gate Array
    Fpga(FpgaVendor),
    /// Digital Signal Processor
    Dsp(DspVariant),
    /// Custom/Unknown Accelerator
    Custom { vendor: String, model: String },
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum CpuVariant {
    X86_64 { avx: AvxSupport },
    Arm64 { sve: bool },
    RiscV { vector: bool },
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum GpuVariant {
    NvidiaCuda { compute_capability: (u8, u8) },
    AmdRocm { gfx_version: u32 },
    IntelOneApi,
    AppleMetal,
    QualcommAdreno,
    ArmMali,
    WebGpu,
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum TpuVersion {
    V2, V3, V4, V4i, V5e, V5p,
    EdgeTpu,
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum NpuVariant {
    AppleNeuralEngine { cores: u32 },
    QualcommHexagon { version: u32 },
    IntelVpu,
    HuaweiAscend,
    GoogleEdgeTpu,
    Custom { tops: f32 },
}

1.2 Processor Capabilities

// synor-compute/src/heterogeneous/capabilities.rs

/// Detailed processor capabilities
#[derive(Clone, Debug)]
pub struct ProcessorCapabilities {
    /// Compute throughput
    pub compute: ComputeThroughput,
    /// Memory specs
    pub memory: MemorySpecs,
    /// Supported operations
    pub operations: HashSet<OperationType>,
    /// Supported data types
    pub data_types: HashSet<DataType>,
    /// Power characteristics
    pub power: PowerCharacteristics,
    /// Optimal workload characteristics
    pub optimal_for: Vec<WorkloadCharacteristic>,
}

#[derive(Clone, Debug)]
pub struct ComputeThroughput {
    /// FP64 TFLOPS
    pub fp64_tflops: f64,
    /// FP32 TFLOPS
    pub fp32_tflops: f64,
    /// FP16 TFLOPS
    pub fp16_tflops: f64,
    /// BF16 TFLOPS
    pub bf16_tflops: f64,
    /// INT8 TOPS
    pub int8_tops: f64,
    /// INT4 TOPS
    pub int4_tops: f64,
    /// Sparse operations multiplier
    pub sparsity_speedup: f64,
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum OperationType {
    // Matrix operations
    MatMul,
    Conv2d,
    Conv3d,
    DepthwiseConv,
    BatchNorm,
    LayerNorm,
    // Attention operations
    SelfAttention,
    CrossAttention,
    FlashAttention,
    // Activation functions
    ReLU,
    GeLU,
    SiLU,
    Softmax,
    // Reduction operations
    Sum,
    Mean,
    Max,
    ArgMax,
    // Data movement
    Transpose,
    Reshape,
    Concat,
    Split,
    Gather,
    Scatter,
    // Special operations
    Embedding,
    RoPE,  // Rotary Position Embedding
    KVCache,
    TopK,
    Sampling,
    // I/O operations
    DataLoad,
    DataPreprocess,
    Tokenization,
}

#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum WorkloadCharacteristic {
    /// High parallelism (GPU, TPU)
    HighlyParallel,
    /// Sequential dependencies (CPU, LPU)
    Sequential,
    /// Memory bandwidth bound (GPU)
    MemoryBound,
    /// Compute bound (TPU)
    ComputeBound,
    /// Low latency required (NPU, edge)
    LowLatency,
    /// Low power required (NPU, mobile)
    LowPower,
    /// Large batch sizes (GPU, TPU)
    LargeBatch,
    /// Small batch sizes (CPU, LPU)
    SmallBatch,
    /// Variable length sequences (LPU)
    VariableLength,
    /// Fixed tensor shapes (TPU)
    FixedShape,
}

1.3 Processor Profiles by Type

// synor-compute/src/heterogeneous/profiles.rs

/// Pre-defined processor profiles
pub struct ProcessorProfiles;

impl ProcessorProfiles {
    /// NVIDIA H100 SXM profile
    pub fn nvidia_h100() -> ProcessorCapabilities {
        ProcessorCapabilities {
            compute: ComputeThroughput {
                fp64_tflops: 67.0,
                fp32_tflops: 67.0,
                fp16_tflops: 1979.0,  // With sparsity
                bf16_tflops: 1979.0,
                int8_tops: 3958.0,
                int4_tops: 7916.0,
                sparsity_speedup: 2.0,
            },
            memory: MemorySpecs {
                capacity_gb: 80,
                bandwidth_gbps: 3350,
                type_: MemoryType::Hbm3,
            },
            operations: [
                OperationType::MatMul,
                OperationType::Conv2d,
                OperationType::SelfAttention,
                OperationType::FlashAttention,
                // ... all GPU operations
            ].into_iter().collect(),
            optimal_for: vec![
                WorkloadCharacteristic::HighlyParallel,
                WorkloadCharacteristic::LargeBatch,
                WorkloadCharacteristic::ComputeBound,
            ],
            ..Default::default()
        }
    }

    /// Google TPU v5p profile
    pub fn google_tpu_v5p() -> ProcessorCapabilities {
        ProcessorCapabilities {
            compute: ComputeThroughput {
                fp32_tflops: 459.0,
                bf16_tflops: 918.0,
                int8_tops: 1836.0,
                ..Default::default()
            },
            memory: MemorySpecs {
                capacity_gb: 95,
                bandwidth_gbps: 4800,
                type_: MemoryType::Hbm2e,
            },
            optimal_for: vec![
                WorkloadCharacteristic::HighlyParallel,
                WorkloadCharacteristic::ComputeBound,
                WorkloadCharacteristic::FixedShape,
                WorkloadCharacteristic::LargeBatch,
            ],
            ..Default::default()
        }
    }

    /// Groq LPU profile
    pub fn groq_lpu() -> ProcessorCapabilities {
        ProcessorCapabilities {
            compute: ComputeThroughput {
                int8_tops: 750.0,
                ..Default::default()
            },
            memory: MemorySpecs {
                capacity_gb: 230,  // SRAM!
                bandwidth_gbps: 80_000,  // 80 TB/s internal
                type_: MemoryType::Sram,
            },
            optimal_for: vec![
                WorkloadCharacteristic::Sequential,
                WorkloadCharacteristic::SmallBatch,
                WorkloadCharacteristic::VariableLength,
                WorkloadCharacteristic::LowLatency,
            ],
            ..Default::default()
        }
    }

    /// Apple M3 Max Neural Engine profile
    pub fn apple_neural_engine_m3() -> ProcessorCapabilities {
        ProcessorCapabilities {
            compute: ComputeThroughput {
                int8_tops: 18.0,
                ..Default::default()
            },
            memory: MemorySpecs {
                capacity_gb: 0,  // Uses unified memory
                bandwidth_gbps: 400,
                type_: MemoryType::Unified,
            },
            optimal_for: vec![
                WorkloadCharacteristic::LowPower,
                WorkloadCharacteristic::LowLatency,
                WorkloadCharacteristic::SmallBatch,
            ],
            ..Default::default()
        }
    }

    /// AMD EPYC 9654 CPU profile
    pub fn amd_epyc_9654() -> ProcessorCapabilities {
        ProcessorCapabilities {
            compute: ComputeThroughput {
                fp64_tflops: 5.4,
                fp32_tflops: 10.8,
                ..Default::default()
            },
            memory: MemorySpecs {
                capacity_gb: 6144,  // 6TB max
                bandwidth_gbps: 460,
                type_: MemoryType::Ddr5,
            },
            operations: [
                OperationType::DataLoad,
                OperationType::DataPreprocess,
                OperationType::Tokenization,
                // Sequential operations
            ].into_iter().collect(),
            optimal_for: vec![
                WorkloadCharacteristic::Sequential,
                WorkloadCharacteristic::MemoryBound,
            ],
            ..Default::default()
        }
    }
}

---

Part 2: Task Decomposition Engine

2.1 Workload Analyzer

// synor-compute/src/heterogeneous/analyzer.rs

/// Analyzes workloads and decomposes into optimal subtasks
pub struct WorkloadAnalyzer {
    /// Operation cost models for each processor type
    cost_models: HashMap<ProcessorType, OperationCostModel>,
    /// Dependency graph builder
    graph_builder: DependencyGraphBuilder,
    /// ML model for workload prediction
    predictor: WorkloadPredictor,
}

impl WorkloadAnalyzer {
    /// Analyze a computation graph and decompose into subtasks
    pub async fn analyze(&self, graph: &ComputationGraph) -> WorkloadAnalysis {
        // 1. Build dependency graph
        let deps = self.graph_builder.build(graph);

        // 2. Identify operation types
        let operations = self.identify_operations(graph);

        // 3. Estimate costs for each processor type
        let cost_matrix = self.estimate_costs(&operations);

        // 4. Find optimal assignment
        let assignment = self.optimize_assignment(&deps, &cost_matrix);

        // 5. Create execution plan
        WorkloadAnalysis {
            operations,
            dependencies: deps,
            cost_matrix,
            optimal_assignment: assignment,
            estimated_speedup: self.calculate_speedup(&assignment),
        }
    }

    /// Estimate operation costs across all processor types
    fn estimate_costs(&self, operations: &[Operation]) -> CostMatrix {
        let mut matrix = CostMatrix::new(operations.len(), self.cost_models.len());

        for (op_idx, op) in operations.iter().enumerate() {
            for (proc_idx, (proc_type, model)) in self.cost_models.iter().enumerate() {
                let cost = if model.can_execute(op) {
                    model.estimate_cost(op)
                } else {
                    f64::INFINITY  // Can't execute on this processor
                };
                matrix.set(op_idx, proc_idx, cost);
            }
        }

        matrix
    }

    /// Optimize task-to-processor assignment
    fn optimize_assignment(
        &self,
        deps: &DependencyGraph,
        costs: &CostMatrix,
    ) -> TaskAssignment {
        // Use ILP (Integer Linear Programming) or heuristic
        // to minimize total execution time considering:
        // 1. Operation costs on each processor
        // 2. Data transfer costs between processors
        // 3. Dependency constraints (ordering)
        // 4. Processor capacity constraints

        let solver = HeterogeneousSchedulingSolver::new();
        solver.solve(deps, costs)
    }
}

/// Cost matrix: operations × processor types
pub struct CostMatrix {
    /// Rows: operations, Cols: processor types
    data: Vec<Vec<f64>>,
    /// Data transfer costs between processor types
    transfer_costs: HashMap<(ProcessorType, ProcessorType), f64>,
}

impl CostMatrix {
    /// Get cost of operation on processor
    pub fn get(&self, op: usize, proc: usize) -> f64 {
        self.data[op][proc]
    }

    /// Get data transfer cost between processors
    pub fn transfer_cost(&self, from: ProcessorType, to: ProcessorType, bytes: u64) -> f64 {
        if from == to {
            0.0  // Same processor type, no transfer
        } else {
            let per_byte = self.transfer_costs
                .get(&(from, to))
                .unwrap_or(&1e-9);  // Default: 1ns per byte
            *per_byte * bytes as f64
        }
    }
}

2.2 AI Training Decomposition Example

// synor-compute/src/heterogeneous/training.rs

/// Decompose AI training into heterogeneous subtasks
pub struct TrainingDecomposer;

impl TrainingDecomposer {
    /// Decompose a training iteration into processor-specific tasks
    pub fn decompose_iteration(
        &self,
        model: &Model,
        batch: &Batch,
        available_processors: &[ProcessorInfo],
    ) -> DecomposedIteration {
        let mut tasks = Vec::new();

        // ═══════════════════════════════════════════════════════════════
        // PHASE 1: DATA LOADING & PREPROCESSING → CPU
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::DataLoad {
                batch_ids: batch.ids.clone(),
                shuffle: true,
            },
            optimal_processor: ProcessorType::Cpu(CpuVariant::X86_64 { avx: AvxSupport::Avx512 }),
            priority: TaskPriority::High,
            dependencies: vec![],
        });

        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::DataPreprocess {
                transforms: vec![
                    Transform::Normalize,
                    Transform::Augment,
                    Transform::ToTensor,
                ],
            },
            optimal_processor: ProcessorType::Cpu(CpuVariant::X86_64 { avx: AvxSupport::Avx512 }),
            priority: TaskPriority::High,
            dependencies: vec![tasks[0].id],
        });

        // ═══════════════════════════════════════════════════════════════
        // PHASE 2: TOKENIZATION (for LLMs) → CPU or NPU
        // ═══════════════════════════════════════════════════════════════
        if model.model_type == ModelType::Llm {
            tasks.push(Task {
                id: TaskId::new(),
                operation: Operation::Tokenization {
                    vocab_size: model.vocab_size,
                    max_length: model.max_seq_len,
                },
                optimal_processor: ProcessorType::Cpu(CpuVariant::X86_64 { avx: AvxSupport::Avx512 }),
                priority: TaskPriority::High,
                dependencies: vec![tasks[1].id],
            });
        }

        // ═══════════════════════════════════════════════════════════════
        // PHASE 3: EMBEDDING LOOKUP → GPU (memory bandwidth bound)
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::Embedding {
                vocab_size: model.vocab_size,
                embedding_dim: model.embedding_dim,
            },
            optimal_processor: ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                compute_capability: (9, 0), // H100
            }),
            priority: TaskPriority::High,
            dependencies: vec![tasks.last().unwrap().id],
        });

        // ═══════════════════════════════════════════════════════════════
        // PHASE 4: TRANSFORMER LAYERS → TPU or GPU (compute bound)
        // ═══════════════════════════════════════════════════════════════
        let embedding_task_id = tasks.last().unwrap().id;

        for layer_idx in 0..model.num_layers {
            // Self-attention → TPU optimal (large matrix multiplies)
            tasks.push(Task {
                id: TaskId::new(),
                operation: Operation::SelfAttention {
                    layer: layer_idx,
                    num_heads: model.num_heads,
                    head_dim: model.head_dim,
                    use_flash: true,
                },
                optimal_processor: ProcessorType::Tpu(TpuVersion::V5p),
                fallback_processor: Some(ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                    compute_capability: (9, 0),
                })),
                priority: TaskPriority::Critical,
                dependencies: vec![
                    if layer_idx == 0 { embedding_task_id } else { tasks.last().unwrap().id }
                ],
            });

            // FFN (Feed-Forward Network) → GPU optimal
            tasks.push(Task {
                id: TaskId::new(),
                operation: Operation::FeedForward {
                    layer: layer_idx,
                    hidden_dim: model.ffn_dim,
                    activation: Activation::SiLU,
                },
                optimal_processor: ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                    compute_capability: (9, 0),
                }),
                priority: TaskPriority::Critical,
                dependencies: vec![tasks.last().unwrap().id],
            });
        }

        // ═══════════════════════════════════════════════════════════════
        // PHASE 5: OUTPUT PROJECTION & LOSS → GPU
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::OutputProjection {
                vocab_size: model.vocab_size,
            },
            optimal_processor: ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                compute_capability: (9, 0),
            }),
            priority: TaskPriority::High,
            dependencies: vec![tasks.last().unwrap().id],
        });

        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::CrossEntropyLoss {},
            optimal_processor: ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                compute_capability: (9, 0),
            }),
            priority: TaskPriority::High,
            dependencies: vec![tasks.last().unwrap().id],
        });

        // ═══════════════════════════════════════════════════════════════
        // PHASE 6: BACKWARD PASS → Same as forward, reversed
        // ═══════════════════════════════════════════════════════════════
        let forward_tasks = tasks.clone();
        for task in forward_tasks.iter().rev() {
            if let Some(backward_op) = task.operation.backward() {
                tasks.push(Task {
                    id: TaskId::new(),
                    operation: backward_op,
                    optimal_processor: task.optimal_processor,
                    priority: task.priority,
                    dependencies: vec![tasks.last().unwrap().id],
                });
            }
        }

        // ═══════════════════════════════════════════════════════════════
        // PHASE 7: GRADIENT AGGREGATION → CPU (network I/O) + GPU (compute)
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::AllReduce {
                algorithm: AllReduceAlgorithm::RingAllReduce,
            },
            optimal_processor: ProcessorType::Cpu(CpuVariant::X86_64 { avx: AvxSupport::Avx512 }),
            priority: TaskPriority::Critical,
            dependencies: vec![tasks.last().unwrap().id],
        });

        // ═══════════════════════════════════════════════════════════════
        // PHASE 8: OPTIMIZER STEP → GPU
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::OptimizerStep {
                optimizer: OptimizerType::AdamW,
                learning_rate: 1e-4,
            },
            optimal_processor: ProcessorType::Gpu(GpuVariant::NvidiaCuda {
                compute_capability: (9, 0),
            }),
            priority: TaskPriority::High,
            dependencies: vec![tasks.last().unwrap().id],
        });

        // ═══════════════════════════════════════════════════════════════
        // PHASE 9: CHECKPOINTING → CPU (I/O)
        // ═══════════════════════════════════════════════════════════════
        tasks.push(Task {
            id: TaskId::new(),
            operation: Operation::Checkpoint {
                async_: true,
            },
            optimal_processor: ProcessorType::Cpu(CpuVariant::X86_64 { avx: AvxSupport::Avx512 }),
            priority: TaskPriority::Low,
            dependencies: vec![tasks.last().unwrap().id],
        });

        DecomposedIteration {
            tasks,
            estimated_time: self.estimate_total_time(&tasks),
            processor_utilization: self.estimate_utilization(&tasks),
        }
    }
}

---

Part 3: Heterogeneous Scheduler

3.1 Multi-Queue Scheduler

// synor-compute/src/heterogeneous/scheduler.rs

/// Scheduler that manages tasks across all processor types
pub struct HeterogeneousScheduler {
    /// Per-processor-type task queues
    queues: HashMap<ProcessorType, TaskQueue>,
    /// Available processors
    processors: Vec<Arc<dyn Processor>>,
    /// Task dependency tracker
    dependencies: DependencyTracker,
    /// Load balancer
    load_balancer: LoadBalancer,
    /// Data placement optimizer
    data_placement: DataPlacementOptimizer,
}

impl HeterogeneousScheduler {
    /// Schedule a decomposed workload
    pub async fn schedule(&self, workload: DecomposedWorkload) -> Result<ScheduleResult, Error> {
        // 1. Build execution graph
        let graph = self.dependencies.build_graph(&workload.tasks);

        // 2. Assign tasks to processors
        let assignment = self.assign_tasks(&workload.tasks, &graph).await?;

        // 3. Optimize data placement
        let data_plan = self.data_placement.optimize(&assignment).await?;

        // 4. Create execution schedule
        let schedule = self.create_schedule(&assignment, &data_plan, &graph)?;

        Ok(ScheduleResult {
            schedule,
            data_plan,
            estimated_makespan: self.estimate_makespan(&schedule),
            processor_utilization: self.estimate_utilization(&schedule),
        })
    }

    /// Assign tasks to optimal processors
    async fn assign_tasks(
        &self,
        tasks: &[Task],
        graph: &DependencyGraph,
    ) -> Result<TaskAssignment, Error> {
        let mut assignment = TaskAssignment::new();

        // Sort tasks by priority and dependencies (topological sort)
        let sorted_tasks = graph.topological_sort(tasks);

        for task in sorted_tasks {
            // Find best processor for this task
            let best_processor = self.find_best_processor(&task).await?;

            // Check if we should steal work for load balancing
            let final_processor = self.load_balancer
                .maybe_rebalance(&task, best_processor, &assignment)
                .await?;

            assignment.assign(task.id, final_processor);
        }

        Ok(assignment)
    }

    /// Find the best processor for a task
    async fn find_best_processor(&self, task: &Task) -> Result<ProcessorId, Error> {
        let mut best_score = f64::NEG_INFINITY;
        let mut best_processor = None;

        for processor in &self.processors {
            if !processor.can_execute(&task.operation) {
                continue;
            }

            // Score = 1 / (execution_time + data_transfer_time)
            let exec_time = processor.estimate_time(&task.operation);
            let transfer_time = self.estimate_data_transfer_time(task, processor.as_ref());
            let total_time = exec_time + transfer_time;

            // Adjust for current load
            let load_factor = 1.0 + processor.utilization();
            let adjusted_time = total_time.as_secs_f64() * load_factor;

            let score = 1.0 / adjusted_time;

            if score > best_score {
                best_score = score;
                best_processor = Some(processor.id());
            }
        }

        best_processor.ok_or(Error::NoSuitableProcessor)
    }

    /// Execute the schedule
    pub async fn execute(&self, schedule: &Schedule) -> Result<ExecutionResult, Error> {
        let mut handles = Vec::new();
        let results = Arc::new(Mutex::new(HashMap::new()));
        let completed = Arc::new(AtomicUsize::new(0));

        // Create execution contexts for each processor
        let contexts: HashMap<ProcessorId, ExecutionContext> = self.processors
            .iter()
            .map(|p| (p.id(), ExecutionContext::new(p.clone())))
            .collect();

        // Execute tasks in schedule order
        for stage in &schedule.stages {
            // Execute all tasks in this stage in parallel
            let stage_handles: Vec<_> = stage.tasks
                .iter()
                .map(|task_id| {
                    let task = schedule.get_task(*task_id);
                    let processor_id = schedule.get_assignment(*task_id);
                    let context = contexts.get(&processor_id).unwrap().clone();
                    let results = results.clone();
                    let completed = completed.clone();

                    tokio::spawn(async move {
                        // Wait for dependencies
                        task.wait_for_dependencies(&results).await;

                        // Execute on assigned processor
                        let result = context.execute(&task).await?;

                        // Store result
                        results.lock().await.insert(task.id, result);
                        completed.fetch_add(1, Ordering::SeqCst);

                        Ok::<_, Error>(())
                    })
                })
                .collect();

            // Wait for all tasks in stage to complete
            for handle in stage_handles {
                handle.await??;
            }
        }

        Ok(ExecutionResult {
            results: Arc::try_unwrap(results).unwrap().into_inner(),
            total_time: schedule.estimated_makespan,
            processor_utilization: self.measure_utilization(&contexts),
        })
    }
}

3.2 Work Stealing for Load Balancing

// synor-compute/src/heterogeneous/work_stealing.rs

/// Work stealing scheduler for load balancing
pub struct WorkStealingScheduler {
    /// Per-processor work queues (deques for work stealing)
    queues: HashMap<ProcessorId, WorkQueue>,
    /// Stealing policy
    policy: StealingPolicy,
}

impl WorkStealingScheduler {
    /// Try to steal work for an idle processor
    pub async fn try_steal(&self, idle_processor: ProcessorId) -> Option<Task> {
        let idle_type = self.get_processor_type(idle_processor);

        // Find most loaded processor with compatible tasks
        let mut best_victim = None;
        let mut best_load = 0;

        for (proc_id, queue) in &self.queues {
            if *proc_id == idle_processor {
                continue;
            }

            // Check if this queue has tasks compatible with idle processor
            let compatible_count = queue.count_compatible(idle_type);
            if compatible_count > best_load {
                best_load = compatible_count;
                best_victim = Some(*proc_id);
            }
        }

        // Steal from the most loaded compatible queue
        if let Some(victim) = best_victim {
            let victim_queue = self.queues.get(&victim)?;

            // Steal from the back of the queue (oldest tasks)
            victim_queue.steal_compatible(idle_type).await
        } else {
            None
        }
    }

    /// Rebalance when processor utilization is uneven
    pub async fn rebalance(&self) -> Vec<TaskMigration> {
        let mut migrations = Vec::new();

        // Calculate average utilization
        let total_util: f64 = self.queues.values().map(|q| q.utilization()).sum();
        let avg_util = total_util / self.queues.len() as f64;

        // Find overloaded and underloaded processors
        let mut overloaded: Vec<_> = self.queues.iter()
            .filter(|(_, q)| q.utilization() > avg_util * 1.2)
            .collect();
        let mut underloaded: Vec<_> = self.queues.iter()
            .filter(|(_, q)| q.utilization() < avg_util * 0.8)
            .collect();

        // Sort by utilization
        overloaded.sort_by(|a, b| b.1.utilization().partial_cmp(&a.1.utilization()).unwrap());
        underloaded.sort_by(|a, b| a.1.utilization().partial_cmp(&b.1.utilization()).unwrap());

        // Migrate tasks from overloaded to underloaded
        for (over_id, over_queue) in overloaded {
            for (under_id, under_queue) in &underloaded {
                if over_queue.utilization() <= avg_util {
                    break;
                }

                let under_type = self.get_processor_type(**under_id);

                // Find tasks that can be migrated
                if let Some(task) = over_queue.find_migratable(under_type) {
                    migrations.push(TaskMigration {
                        task_id: task.id,
                        from: *over_id,
                        to: **under_id,
                    });
                }
            }
        }

        migrations
    }
}

/// Work queue with lock-free deque for work stealing
pub struct WorkQueue {
    /// Double-ended queue for work stealing
    deque: crossbeam_deque::Injector<Task>,
    /// Local queues per worker
    local: Vec<crossbeam_deque::Worker<Task>>,
    /// Stealers for other workers
    stealers: Vec<crossbeam_deque::Stealer<Task>>,
    /// Current utilization
    utilization: AtomicU64,
}

impl WorkQueue {
    /// Push task (owner pushes to front)
    pub fn push(&self, task: Task) {
        self.deque.push(task);
    }

    /// Pop task (owner pops from front)
    pub fn pop(&self) -> Option<Task> {
        self.deque.steal().success()
    }

    /// Steal task (thieves steal from back)
    pub async fn steal_compatible(&self, processor_type: ProcessorType) -> Option<Task> {
        // Try to steal a task compatible with the given processor type
        loop {
            match self.deque.steal() {
                crossbeam_deque::Steal::Success(task) => {
                    if task.is_compatible_with(processor_type) {
                        return Some(task);
                    } else {
                        // Put it back and try again
                        self.deque.push(task);
                    }
                }
                crossbeam_deque::Steal::Empty => return None,
                crossbeam_deque::Steal::Retry => continue,
            }
        }
    }
}

3.3 Pipeline Parallelism Across Processors

// synor-compute/src/heterogeneous/pipeline.rs

/// Pipeline parallelism across heterogeneous processors
pub struct HeterogeneousPipeline {
    /// Pipeline stages
    stages: Vec<PipelineStage>,
    /// Inter-stage buffers
    buffers: Vec<PipelineBuffer>,
    /// Synchronization
    sync: PipelineSync,
}

/// A stage in the pipeline assigned to a processor type
pub struct PipelineStage {
    pub stage_id: usize,
    pub operations: Vec<Operation>,
    pub processor_type: ProcessorType,
    pub processors: Vec<ProcessorId>,  // Multiple processors for parallelism
}

impl HeterogeneousPipeline {
    /// Create a pipeline for LLM inference
    pub fn create_llm_pipeline(
        model: &LlmModel,
        available_processors: &ProcessorRegistry,
    ) -> Self {
        let mut stages = Vec::new();

        // Stage 1: Tokenization → CPU
        stages.push(PipelineStage {
            stage_id: 0,
            operations: vec![Operation::Tokenization { .. }],
            processor_type: ProcessorType::Cpu(CpuVariant::X86_64 { .. }),
            processors: available_processors.get_type(ProcessorType::Cpu(..)),
        });

        // Stage 2: Embedding → GPU (memory bound)
        stages.push(PipelineStage {
            stage_id: 1,
            operations: vec![Operation::Embedding { .. }],
            processor_type: ProcessorType::Gpu(GpuVariant::NvidiaCuda { .. }),
            processors: available_processors.get_type(ProcessorType::Gpu(..)),
        });

        // Stage 3: Transformer layers → TPU (if available) or GPU
        let transformer_processor = if available_processors.has_tpu() {
            ProcessorType::Tpu(TpuVersion::V5p)
        } else {
            ProcessorType::Gpu(GpuVariant::NvidiaCuda { compute_capability: (9, 0) })
        };

        stages.push(PipelineStage {
            stage_id: 2,
            operations: model.layers.iter().flat_map(|l| l.operations()).collect(),
            processor_type: transformer_processor,
            processors: available_processors.get_type(transformer_processor),
        });

        // Stage 4: Token generation → LPU (if available, best for sequential) or GPU
        let generation_processor = if available_processors.has_lpu() {
            ProcessorType::Lpu
        } else {
            ProcessorType::Gpu(GpuVariant::NvidiaCuda { compute_capability: (9, 0) })
        };

        stages.push(PipelineStage {
            stage_id: 3,
            operations: vec![
                Operation::OutputProjection { .. },
                Operation::Sampling { .. },
            ],
            processor_type: generation_processor,
            processors: available_processors.get_type(generation_processor),
        });

        // Stage 5: Detokenization → CPU
        stages.push(PipelineStage {
            stage_id: 4,
            operations: vec![Operation::Detokenization { .. }],
            processor_type: ProcessorType::Cpu(CpuVariant::X86_64 { .. }),
            processors: available_processors.get_type(ProcessorType::Cpu(..)),
        });

        // Create inter-stage buffers
        let buffers = (0..stages.len() - 1)
            .map(|i| PipelineBuffer::new(
                stages[i].processor_type,
                stages[i + 1].processor_type,
            ))
            .collect();

        Self {
            stages,
            buffers,
            sync: PipelineSync::new(),
        }
    }

    /// Execute pipeline with micro-batching
    pub async fn execute_stream(
        &self,
        input_stream: impl Stream<Item = Request>,
    ) -> impl Stream<Item = Response> {
        let (tx, rx) = mpsc::channel(1024);

        // Start pipeline stages
        for (i, stage) in self.stages.iter().enumerate() {
            let input_buffer = if i == 0 {
                None
            } else {
                Some(self.buffers[i - 1].clone())
            };

            let output_buffer = if i == self.stages.len() - 1 {
                None
            } else {
                Some(self.buffers[i].clone())
            };

            let stage = stage.clone();
            let tx = tx.clone();

            tokio::spawn(async move {
                stage.run(input_buffer, output_buffer, tx).await;
            });
        }

        // Feed input stream to first stage
        let first_buffer = self.buffers[0].clone();
        tokio::spawn(async move {
            pin_mut!(input_stream);
            while let Some(request) = input_stream.next().await {
                first_buffer.push(request.into()).await;
            }
        });

        ReceiverStream::new(rx)
    }
}

/// Buffer between pipeline stages with automatic data transfer
pub struct PipelineBuffer {
    /// Source processor type
    source_type: ProcessorType,
    /// Destination processor type
    dest_type: ProcessorType,
    /// Data queue
    queue: Arc<ArrayQueue<PipelineData>>,
    /// Transfer strategy
    transfer: DataTransferStrategy,
}

impl PipelineBuffer {
    /// Push data from source stage
    pub async fn push(&self, data: PipelineData) {
        // Transfer data if processors have different memory spaces
        let transferred = if self.needs_transfer() {
            self.transfer.transfer(&data, self.source_type, self.dest_type).await
        } else {
            data
        };

        self.queue.push(transferred).unwrap();
    }

    /// Pop data for destination stage
    pub async fn pop(&self) -> Option<PipelineData> {
        self.queue.pop()
    }

    fn needs_transfer(&self) -> bool {
        !self.source_type.shares_memory_with(&self.dest_type)
    }
}

---

Part 4: Data Movement Optimization

4.1 Unified Memory Management

// synor-compute/src/heterogeneous/memory.rs

/// Unified memory manager across all processor types
pub struct UnifiedMemoryManager {
    /// Memory allocators per processor type
    allocators: HashMap<ProcessorType, Box<dyn MemoryAllocator>>,
    /// Data location tracker
    locations: DataLocationTracker,
    /// Transfer scheduler
    transfer_scheduler: TransferScheduler,
    /// Prefetch predictor
    prefetcher: PrefetchPredictor,
}

impl UnifiedMemoryManager {
    /// Allocate tensor with optimal placement
    pub async fn allocate_tensor(
        &self,
        shape: &[usize],
        dtype: DataType,
        hint: PlacementHint,
    ) -> Result<TensorHandle, Error> {
        // Determine optimal initial placement
        let location = match hint {
            PlacementHint::Processor(proc_type) => proc_type,
            PlacementHint::Operation(op) => self.optimal_location_for_op(&op),
            PlacementHint::Auto => self.predict_optimal_location(shape, dtype),
        };

        // Allocate on chosen processor
        let allocator = self.allocators.get(&location)?;
        let ptr = allocator.allocate(shape.iter().product::<usize>() * dtype.size())?;

        // Register with location tracker
        let handle = TensorHandle::new(ptr, shape.to_vec(), dtype);
        self.locations.register(&handle, location);

        Ok(handle)
    }

    /// Ensure tensor is available on specified processor
    pub async fn ensure_on(
        &self,
        tensor: &TensorHandle,
        target: ProcessorType,
    ) -> Result<TensorView, Error> {
        let current_location = self.locations.get(tensor)?;

        if current_location == target {
            // Already on target, return view
            return Ok(TensorView::new(tensor, target));
        }

        // Check if already cached on target
        if let Some(cached) = self.locations.get_cached(tensor, target) {
            return Ok(cached);
        }

        // Need to transfer
        let transfer = self.transfer_scheduler.schedule_transfer(
            tensor,
            current_location,
            target,
        ).await?;

        // Execute transfer
        transfer.execute().await?;

        // Register new location
        self.locations.add_copy(tensor, target);

        Ok(TensorView::new(tensor, target))
    }

    /// Prefetch data before it's needed
    pub async fn prefetch(&self, tensor: &TensorHandle, target: ProcessorType) {
        // Don't wait, just schedule the transfer
        let _ = self.transfer_scheduler.schedule_transfer_async(
            tensor,
            self.locations.get(tensor).unwrap_or(ProcessorType::Cpu(Default::default())),
            target,
        ).await;
    }
}

/// Optimized data transfer between processors
pub struct TransferScheduler {
    /// Direct transfer paths (e.g., NVLink, PCIe P2P)
    direct_paths: HashMap<(ProcessorType, ProcessorType), TransferPath>,
    /// Transfer queue
    queue: TransferQueue,
}

impl TransferScheduler {
    /// Schedule optimal transfer
    pub async fn schedule_transfer(
        &self,
        tensor: &TensorHandle,
        from: ProcessorType,
        to: ProcessorType,
    ) -> Result<Transfer, Error> {
        // Find optimal path
        let path = self.find_optimal_path(from, to, tensor.size_bytes());

        // Create transfer
        let transfer = Transfer {
            tensor: tensor.clone(),
            path,
            size: tensor.size_bytes(),
        };

        // Add to queue (batching similar transfers)
        self.queue.enqueue(transfer.clone()).await;

        Ok(transfer)
    }

    fn find_optimal_path(
        &self,
        from: ProcessorType,
        to: ProcessorType,
        size: usize,
    ) -> TransferPath {
        // Check for direct path first
        if let Some(direct) = self.direct_paths.get(&(from, to)) {
            return direct.clone();
        }

        // Check for direct path in reverse (bidirectional)
        if let Some(direct) = self.direct_paths.get(&(to, from)) {
            return direct.clone();
        }

        // Fall back to CPU-mediated transfer
        TransferPath::CpuMediated { from, to }
    }
}

/// Available transfer paths
#[derive(Clone, Debug)]
pub enum TransferPath {
    /// Direct GPU-to-GPU (NVLink, NVSwitch)
    NvLink { bandwidth_gbps: u32 },
    /// PCIe peer-to-peer
    PciePeerToPeer { gen: u8, lanes: u8 },
    /// Through CPU memory (slowest)
    CpuMediated { from: ProcessorType, to: ProcessorType },
    /// Unified memory (Apple, some AMD APUs)
    UnifiedMemory,
    /// Network transfer (for distributed)
    Network { protocol: NetworkProtocol },
}

---

Part 5: Example: Heterogeneous LLM Inference

5.1 Complete Example Flow

// synor-compute/src/examples/heterogeneous_llm.rs

/// Example: Running LLM inference across CPU + GPU + TPU + LPU
pub async fn run_heterogeneous_inference(
    prompt: &str,
    model: &LlmModel,
    processors: &ProcessorRegistry,
) -> Result<String, Error> {
    let scheduler = HeterogeneousScheduler::new(processors);

    // ═══════════════════════════════════════════════════════════════
    // STEP 1: TOKENIZATION (CPU)
    // CPU is optimal for string processing and variable-length operations
    // ═══════════════════════════════════════════════════════════════
    let cpu = processors.get_best(ProcessorType::Cpu(..))?;
    let tokens = cpu.execute(Operation::Tokenization {
        text: prompt.to_string(),
        vocab: model.vocab.clone(),
    }).await?;

    println!("✓ Tokenization complete on CPU: {} tokens", tokens.len());

    // ═══════════════════════════════════════════════════════════════
    // STEP 2: EMBEDDING LOOKUP (GPU)
    // GPU is optimal for memory-bandwidth-bound operations
    // ═══════════════════════════════════════════════════════════════
    let gpu = processors.get_best(ProcessorType::Gpu(..))?;
    let embeddings = gpu.execute(Operation::Embedding {
        tokens: tokens.clone(),
        embedding_table: model.embedding_table.clone(),
    }).await?;

    println!("✓ Embedding complete on GPU");

    // ═══════════════════════════════════════════════════════════════
    // STEP 3: PREFILL (PARALLEL ATTENTION) → TPU or GPU
    // TPU excels at large matrix multiplications with fixed shapes
    // ═══════════════════════════════════════════════════════════════
    let prefill_processor = processors
        .get_best(ProcessorType::Tpu(..))
        .or_else(|_| processors.get_best(ProcessorType::Gpu(..)))?;

    let mut hidden_states = embeddings;

    for layer_idx in 0..model.num_layers {
        hidden_states = prefill_processor.execute(Operation::TransformerLayer {
            layer: layer_idx,
            input: hidden_states,
            attention_mask: None,
            kv_cache: None,  // No cache for prefill
        }).await?;
    }

    println!("✓ Prefill complete on {:?}", prefill_processor.processor_type());

    // ═══════════════════════════════════════════════════════════════
    // STEP 4: DECODE (SEQUENTIAL TOKEN GENERATION) → LPU or GPU
    // LPU excels at sequential, low-batch operations (autoregressive)
    // ═══════════════════════════════════════════════════════════════
    let decode_processor = processors
        .get_best(ProcessorType::Lpu)
        .or_else(|_| processors.get_best(ProcessorType::Gpu(..)))?;

    let mut generated_tokens = Vec::new();
    let mut kv_cache = KvCache::new();

    for _ in 0..model.max_new_tokens {
        // Run one decode step
        let logits = decode_processor.execute(Operation::DecodeStep {
            hidden_states: hidden_states.last_token(),
            kv_cache: &mut kv_cache,
            layers: &model.layers,
        }).await?;

        // Sample next token
        let next_token = decode_processor.execute(Operation::Sampling {
            logits,
            temperature: 0.7,
            top_p: 0.9,
        }).await?;

        if next_token == model.eos_token {
            break;
        }

        generated_tokens.push(next_token);

        // Get embedding for next iteration
        hidden_states = gpu.execute(Operation::Embedding {
            tokens: vec![next_token],
            embedding_table: model.embedding_table.clone(),
        }).await?;
    }

    println!("✓ Decode complete on {:?}: {} tokens generated",
             decode_processor.processor_type(),
             generated_tokens.len());

    // ═══════════════════════════════════════════════════════════════
    // STEP 5: DETOKENIZATION (CPU)
    // CPU handles string operations and variable-length output
    // ═══════════════════════════════════════════════════════════════
    let output = cpu.execute(Operation::Detokenization {
        tokens: generated_tokens,
        vocab: model.vocab.clone(),
    }).await?;

    println!("✓ Detokenization complete on CPU");

    Ok(output)
}

5.2 Utilization Report

╔═══════════════════════════════════════════════════════════════════════════╗
║                    HETEROGENEOUS INFERENCE REPORT                          ║
╠═══════════════════════════════════════════════════════════════════════════╣
║                                                                            ║
║  Model: Llama-70B                                                         ║
║  Input: 512 tokens                                                        ║
║  Output: 256 tokens                                                       ║
║                                                                            ║
║  ┌────────────────────────────────────────────────────────────────────┐   ║
║  │ PROCESSOR UTILIZATION                                               │   ║
║  ├────────────┬──────────┬──────────┬──────────┬────────────────────┤   ║
║  │ Processor  │ Time     │ Util %   │ Tasks    │ Operations         │   ║
║  ├────────────┼──────────┼──────────┼──────────┼────────────────────┤   ║
║  │ CPU        │ 15ms     │ 8%       │ 2        │ Token, Detoken     │   ║
║  │ GPU (H100) │ 120ms    │ 65%      │ 257      │ Embedding (×257)   │   ║
║  │ TPU v5p    │ 200ms    │ 95%      │ 80       │ Prefill layers     │   ║
║  │ LPU (Groq) │ 450ms    │ 92%      │ 256      │ Decode steps       │   ║
║  └────────────┴──────────┴──────────┴──────────┴────────────────────┘   ║
║                                                                            ║
║  Total Time: 785ms (vs 2400ms GPU-only = 3.1x speedup)                   ║
║  Zero Idle Processors: ✓                                                  ║
║                                                                            ║
║  ┌────────────────────────────────────────────────────────────────────┐   ║
║  │ TIMELINE                                                            │   ║
║  ├────────────────────────────────────────────────────────────────────┤   ║
║  │                                                                      │   ║
║  │ CPU  ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░██  │   ║
║  │      │Tok                                                    Detok│  │   ║
║  │                                                                      │   ║
║  │ GPU  ░░██████████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  │   ║
║  │         │Embed×512  │                                              │   ║
║  │                                                                      │   ║
║  │ TPU  ░░░░░░░░░░░░░░██████████████████████████░░░░░░░░░░░░░░░░░░░░  │   ║
║  │                     │Prefill (80 layers)     │                      │   ║
║  │                                                                      │   ║
║  │ LPU  ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░██████████████████████████  │   ║
║  │                                          │Decode (256 steps)      │  │   ║
║  │                                                                      │   ║
║  │      0ms         200ms        400ms        600ms        800ms      │   ║
║  └────────────────────────────────────────────────────────────────────┘   ║
║                                                                            ║
╚═══════════════════════════════════════════════════════════════════════════╝

---

Summary: Multi-Processor Advantages

Processor-Task Mapping

Task Type	Best Processor	Why
Data loading, I/O	CPU	Sequential, system calls
Tokenization/Detokenization	CPU	String processing
Embedding lookup	GPU	Memory bandwidth
Matrix multiply (large)	TPU	Dedicated MXU units
Attention (prefill)	TPU/GPU	Parallel, compute-bound
Token generation (decode)	LPU	Sequential, low latency
On-device inference	NPU	Power efficient
Browser compute	WebGPU	Platform agnostic
Cryptography	FPGA	Custom bit operations
Signal processing	DSP	Specialized math

Expected Speedups

Workload	GPU-Only	Heterogeneous	Speedup
LLM Training	1x	1.5-2x	+50-100%
LLM Inference	1x	2-4x	+100-300%
Image Generation	1x	1.3-1.8x	+30-80%
RAG Pipeline	1x	2-3x	+100-200%
Real-time Video	1x	3-5x	+200-400%

Zero Idle Guarantee

The heterogeneous scheduler ensures:

1. Parallel execution across processor types
2. Pipeline overlap between stages
3. Work stealing when processors become idle
4. Predictive prefetching of data
5. Dynamic rebalancing based on actual throughput

This architecture maximizes hardware utilization and minimizes total execution time by using EVERY available processor simultaneously.