//! Backend Selection: GPU vs SIMD Cost-Based Dispatcher
//!
//! This example demonstrates Trueno-DB's physics-based cost model for
//! selecting between GPU and SIMD backends based on arithmetic intensity.
//!
//! Algorithm: 5x Rule (Toyota Way: Genchi Genbutsu - Go and See)
//! - GPU compute must be > 5x `PCIe` transfer time to be worthwhile
//! - Based on real-world measurements of GPU transfer overhead
//!
//! Run with: cargo run --example `backend_selection`

use trueno_db::backend::BackendDispatcher;

fn main() {
    println!("=== Trueno-DB Backend Selection (Cost-Based Dispatcher) ===\n");

    println!("Physics-Based Cost Model:");
    println!("  PCIe Gen4 x16 bandwidth: 32 GB/s");
    println!("  GPU compute throughput: 100 GFLOP/s (conservative estimate)");
    println!("  Minimum GPU data size: 10 MB");
    println!("  5x Rule: GPU only if compute > 5x transfer time\n");

    println!("=== Test Case 1: Small Dataset (1 MB) ===");
    let data_size_mb = 1.0;
    let data_bytes = (data_size_mb * 1_048_576.0) as usize;
    let flops = 1_000_000.0; // 1M FLOPs (simple aggregation)

    let backend = BackendDispatcher::select(data_bytes, flops);
    println!("  Data size: {data_size_mb:.1} MB");
    println!("  Estimated FLOPs: {flops:.0}");
    println!("  Selected backend: {backend:?}");
    println!("  Rationale: Below 10 MB threshold → SIMD\n");

    println!("=== Test Case 2: Medium Dataset (50 MB, Low Compute) ===");
    let data_size_mb = 50.0;
    let data_bytes = (data_size_mb * 1_048_576.0) as usize;
    let flops = 10_000_000.0; // 10M FLOPs

    let pcie_transfer_ms = (data_bytes as f64 / (32.0 * 1_000_000_000.0)) * 1000.0;
    let gpu_compute_ms = (flops / (100.0 * 1_000_000_000.0)) * 1000.0;
    let backend = BackendDispatcher::select(data_bytes, flops);

    println!("  Data size: {data_size_mb:.1} MB");
    println!("  Estimated FLOPs: {flops:.0}");
    println!("  PCIe transfer time: {pcie_transfer_ms:.3} ms");
    println!("  GPU compute time: {gpu_compute_ms:.3} ms");
    println!("  Ratio: {:.2}x (compute / transfer)", gpu_compute_ms / pcie_transfer_ms);
    println!("  Selected backend: {backend:?}");
    println!("  Rationale: Compute < 5x transfer → SIMD (transfer overhead too high)\n");

    println!("=== Test Case 3: Large Dataset (100 MB, High Compute) ===");
    let data_size_mb = 100.0;
    let data_bytes = (data_size_mb * 1_048_576.0) as usize;
    let flops = 1_000_000_000.0; // 1 GFLOP

    let pcie_transfer_ms = (data_bytes as f64 / (32.0 * 1_000_000_000.0)) * 1000.0;
    let gpu_compute_ms = (flops / (100.0 * 1_000_000_000.0)) * 1000.0;
    let backend = BackendDispatcher::select(data_bytes, flops);

    println!("  Data size: {data_size_mb:.1} MB");
    println!("  Estimated FLOPs: {flops:.0}");
    println!("  PCIe transfer time: {pcie_transfer_ms:.3} ms");
    println!("  GPU compute time: {gpu_compute_ms:.3} ms");
    println!("  Ratio: {:.2}x (compute / transfer)", gpu_compute_ms / pcie_transfer_ms);
    println!("  Selected backend: {backend:?}");
    println!("  Rationale: Compute > 5x transfer → GPU (transfer overhead amortized)\n");

    println!("=== Test Case 4: Very Large Dataset (1 GB, Complex Query) ===");
    let data_size_mb = 1024.0;
    let data_bytes = (data_size_mb * 1_048_576.0) as usize;
    let flops = 50_000_000_000.0; // 50 GFLOPs (complex aggregation with GROUP BY)

    let pcie_transfer_ms = (data_bytes as f64 / (32.0 * 1_000_000_000.0)) * 1000.0;
    let gpu_compute_ms = (flops / (100.0 * 1_000_000_000.0)) * 1000.0;
    let backend = BackendDispatcher::select(data_bytes, flops);

    println!("  Data size: {data_size_mb:.1} MB");
    println!("  Estimated FLOPs: {flops:.0}");
    println!("  PCIe transfer time: {pcie_transfer_ms:.1} ms");
    println!("  GPU compute time: {gpu_compute_ms:.1} ms");
    println!("  Ratio: {:.2}x (compute / transfer)", gpu_compute_ms / pcie_transfer_ms);
    println!("  Selected backend: {backend:?}");
    println!("  Rationale: Large dataset + high compute intensity → GPU sweet spot\n");

    println!("=== Algorithm Summary ===");
    println!("Decision tree:");
    println!("  1. If data < 10 MB → SIMD (transfer overhead dominates)");
    println!("  2. Calculate PCIe transfer time = bytes / 32 GB/s");
    println!("  3. Estimate GPU compute time = FLOPs / 100 GFLOP/s");
    println!("  4. If compute > 5x transfer → GPU");
    println!("  5. Otherwise → SIMD\n");

    println!("=== Backend Implementation Status ===");
    println!("Phase 1 MVP (v0.1.0):");
    println!("  ✓ Backend dispatcher (cost-based selection logic)");
    println!("  ✓ SIMD backend integration (via trueno crate)");
    println!("  ✗ GPU kernels (deferred to Phase 2)\n");

    println!("Phase 2 (GPU Kernel Implementation):");
    println!("  - Actual wgpu compute shaders");
    println!("  - GPU device initialization");
    println!("  - PCIe bandwidth runtime calibration");
    println!("  - Multi-GPU data partitioning\n");

    println!("=== SIMD Backend (Currently Available) ===");
    println!("Trueno crate provides SIMD acceleration:");
    println!("  - AVX-512 (if available)");
    println!("  - AVX2 fallback");
    println!("  - SSE2 fallback");
    println!("  - Scalar fallback");
    println!("  - Auto-detection at runtime\n");

    println!("Example: Run with SIMD backend");
    println!("  cargo run --example simd_acceleration\n");
}
