5.1 The Compilation Pipeline

Transforming high-level operations into efficient accelerator code requires a sophisticated compilation pipeline. WIA-AI-011 defines a multi-stage compilation process: frontend parsing, intermediate representation (IR) generation, optimization passes, backend code generation, and device-specific assembly. Each stage plays a crucial role in achieving optimal performance while maintaining portability across diverse accelerator architectures.

The compilation pipeline serves as the bridge between human-readable code and machine-executable instructions. Modern AI accelerators feature diverse architectures—systolic arrays, vector processors, spatial architectures, and hybrid designs. A well-designed compilation pipeline must abstract these differences while enabling hardware-specific optimizations.

Source Code → Frontend → IR → Optimization → Backend → Assembly → Binary
                                  ↓
                        Device-specific tuning
                                  ↓
                        Performance Analysis
                                  ↓
                        Iterative Refinement

The frontend stage parses source code and performs semantic analysis. For WIA-AI-011, frontends may accept PyTorch models, TensorFlow graphs, ONNX representations, or domain-specific languages. The frontend validates correctness, infers types and shapes, and constructs an initial IR representation.

// Frontend compilation example
wia_compiler_t* compiler = wia_create_compiler(WIA_BACKEND_NPU);

// Configure compiler options
wia_compiler_config_t config = {
    .optimization_level = WIA_OPT_LEVEL_3,
    .target_precision = WIA_FLOAT16,
    .enable_fusion = true,
    .enable_auto_tuning = true,
    .max_compilation_time = 300  // seconds
};

wia_set_compiler_config(compiler, &config);

// Compile from PyTorch model
wia_compiled_module_t* module = wia_compile_from_pytorch(
    compiler, "model.pt", &config);

// Or compile from ONNX
wia_compiled_module_t* onnx_module = wia_compile_from_onnx(
    compiler, "model.onnx", &config);

5.2 Intermediate Representations

A well-designed IR enables portable optimization while abstracting hardware details. WIA-AI-011 adopts a multi-level IR approach inspired by MLIR (Multi-Level Intermediate Representation). The high-level IR represents tensor operations with shape and type information. The mid-level IR introduces explicit memory operations and control flow. The low-level IR exposes hardware-specific instructions for final optimization.

This multi-level approach provides flexibility at each abstraction layer. High-level optimizations like operator fusion and algebraic simplification operate on tensor semantics. Mid-level optimizations focus on memory layout, tiling strategies, and parallelization. Low-level optimizations leverage specific hardware capabilities like specialized instructions or memory hierarchies.

// High-level IR - Tensor operations
%result = tensor.matmul %A, %B : (tensor<1024x512xf32>, tensor<512x256xf32>)
                                 -> tensor<1024x256xf32>
%biased = tensor.add %result, %bias : (tensor<1024x256xf32>, tensor<256xf32>)
                                      -> tensor<1024x256xf32>
%activated = tensor.relu %biased : tensor<1024x256xf32> -> tensor<1024x256xf32>

// Mid-level IR - Explicit memory and control flow
%buffer_a = memref.alloc() : memref<1024x512xf32>
%buffer_b = memref.alloc() : memref<512x256xf32>
%buffer_out = memref.alloc() : memref<1024x256xf32>

affine.for %i = 0 to 1024 step 64 {
  affine.for %j = 0 to 256 step 64 {
    affine.for %k = 0 to 512 step 64 {
      linalg.matmul
        ins(%buffer_a, %buffer_b : memref<1024x512xf32>, memref<512x256xf32>)
        outs(%buffer_out : memref<1024x256xf32>)
        tile_sizes = [64, 64, 64]
    }
  }
}

// Low-level IR (device-specific for NPU)
npu.dma_copy %host_a -> %device_a, size=2097152
npu.dma_copy %host_b -> %device_b, size=524288
npu.sync

npu.tile_matmul_fused %device_a, %device_b, %device_bias, %device_out,
  tile_size=64x64x64,
  precision=fp16,
  activation=relu,
  use_tensor_cores=true

npu.sync
npu.dma_copy %device_out -> %host_out, size=1048576

Each IR level uses appropriate dialects and abstractions. The high-level IR uses tensor dialects with implicit parallelism. The mid-level IR uses affine and linalg dialects with explicit loop structures. The low-level IR uses hardware-specific dialects exposing device instructions.

5.2.1 IR Validation and Verification

Compilation passes must preserve semantics across transformations. WIA-AI-011 compilers implement verification passes that check IR invariants: type safety, shape compatibility, memory access legality, and resource constraints. These checks catch bugs early in the compilation process.

// IR verification example
wia_ir_module_t* ir = wia_get_ir_module(compiler, WIA_IR_LEVEL_MID);

wia_verification_result_t result;
if (!wia_verify_ir(ir, &result)) {
    fprintf(stderr, "IR verification failed:\n");
    fprintf(stderr, "  Error: %s\n", result.error_message);
    fprintf(stderr, "  Location: %s:%d\n", result.file, result.line);
    wia_print_ir_context(ir, result.location, 5);  // Show 5 lines context
}

5.3 Operator Fusion

Fusing multiple operations into single kernels reduces memory traffic and launch overhead. Element-wise operations following matrix multiplications or convolutions are prime fusion candidates. WIA-AI-011 compilers automatically detect fusion opportunities through pattern matching and cost models.

Consider a typical neural network layer: matrix multiplication followed by bias addition and activation. Without fusion, this requires three kernel launches and two intermediate memory writes. With fusion, all operations execute in a single kernel with one memory write, dramatically improving performance.

// Unfused: Three kernel launches, two memory writes
Y = MatMul(A, B)        // Write Y to memory
Z = Bias(Y, bias)       // Read Y, write Z
Out = ReLU(Z)           // Read Z, write Out

// Performance characteristics:
// - 3 kernel launches (overhead: ~30-50 μs each)
// - Memory traffic: Write Y (4 MB) + Write Z (4 MB) + Write Out (4 MB) = 12 MB
// - Latency: Launch + 3x memory latency

// Fused: One kernel, one memory write
Out = FusedMatMulBiasReLU(A, B, bias)

// Performance characteristics:
// - 1 kernel launch (overhead: ~30-50 μs)
// - Memory traffic: Write Out (4 MB) = 4 MB  (3x reduction!)
// - Latency: Launch + 1x memory latency
// - Speedup: Typically 2-3x for small to medium matrices

5.3.1 Fusion Pattern Library

WIA-AI-011 compilers maintain libraries of fusion patterns. Common patterns include:

• Conv-Bias-Activation: Convolutional layer with bias and activation (ReLU, Sigmoid, etc.)
• MatMul-Bias-Activation: Linear layer with bias and activation
• Batch Normalization Fusion: Conv/MatMul followed by batch norm and activation
• Attention Pattern Fusion: Q·K^T → Scale → Softmax → Dropout → ·V fused into single kernel
• Residual Connection Fusion: Operation followed by element-wise addition with skip connection

// Advanced fusion example: Multi-head attention
// Pattern recognition and fusion
def fused_multihead_attention(Q, K, V, mask, dropout_rate):
    """
    Unfused operations:
    1. scores = Q @ K.T          (matmul)
    2. scores = scores / sqrt(d)  (scale)
    3. scores = scores + mask     (masked addition)
    4. probs = softmax(scores)    (softmax)
    5. probs = dropout(probs)     (dropout)
    6. output = probs @ V         (matmul)

    Fused into single kernel with intermediate results in registers/cache
    """
    return wia_fused_attention_kernel(Q, K, V, mask, dropout_rate)

// Compiler automatically recognizes this pattern
wia_fusion_pattern_t attention_pattern = {
    .pattern_name = "multihead_attention",
    .operations = {
        WIA_OP_MATMUL,
        WIA_OP_SCALE,
        WIA_OP_MASKED_ADD,
        WIA_OP_SOFTMAX,
        WIA_OP_DROPOUT,
        WIA_OP_MATMUL
    },
    .num_operations = 6,
    .expected_speedup = 2.5,  // Based on profiling data
    .memory_reduction = 0.75  // 75% less memory traffic
};

wia_register_fusion_pattern(compiler, &attention_pattern);

5.4 Auto-Tuning and Kernel Selection

Optimal kernel parameters vary by problem size, data layout, and hardware characteristics. Auto-tuning explores the configuration space through systematic search or machine learning models. WIA-AI-011 supports both offline tuning (during compilation) and runtime adaptive tuning based on actual execution profiles.

The parameter space for kernel optimization is vast. For a matrix multiplication kernel alone, tuning parameters include tile sizes, unroll factors, vectorization width, prefetch strategies, register blocking, and instruction scheduling. Exhaustive search is impractical; intelligent exploration is essential.

wia_autotune_config_t config = {
    .method = WIA_AUTOTUNE_GENETIC,
    .time_budget_seconds = 300,
    .population_size = 50,
    .num_generations = 100,
    .mutation_rate = 0.1,

    .parameter_space = {
        .tile_sizes = {16, 32, 64, 128, 256},
        .unroll_factors = {1, 2, 4, 8, 16},
        .vectorization_width = {4, 8, 16, 32},
        .prefetch_distance = {0, 1, 2, 4, 8},
        .register_blocking = {1, 2, 4, 8}
    },

    .optimization_objective = WIA_OBJECTIVE_THROUGHPUT,
    .constraints = {
        .max_register_usage = 64,
        .max_shared_memory = 49152,  // bytes
        .max_latency_ms = 10.0
    }
};

wia_autotune_kernel(ctx, kernel_spec, &config, &optimized_kernel);

// Save tuned kernel to cache for reuse
wia_cache_kernel(optimized_kernel, "matmul_1024x1024_fp16.wiak");

5.4.1 Machine Learning-Based Tuning

Advanced auto-tuners use machine learning to predict optimal configurations without exhaustive search. These models learn from historical tuning data, generalizing across similar kernels and problem sizes.

// ML-based auto-tuning
wia_ml_autotuner_t* ml_tuner = wia_create_ml_autotuner();

// Load pre-trained model (trained on thousands of kernels)
wia_load_autotuner_model(ml_tuner, "wia_tuner_model_v2.pb");

// Predict optimal configuration
wia_kernel_config_t predicted_config;
wia_predict_kernel_config(ml_tuner, kernel_spec, &predicted_config);

// Optionally refine with limited search
wia_refine_config_local_search(ml_tuner, kernel_spec,
                                &predicted_config,
                                refinement_budget_seconds=30);

printf("Predicted tile size: %dx%d\n",
       predicted_config.tile_m, predicted_config.tile_n);
printf("Predicted performance: %.2f TFLOPS\n",
       predicted_config.estimated_tflops);

5.5 Memory Access Optimization

Memory bandwidth often limits performance more than compute throughput. Optimizations include tiling to improve cache locality, memory coalescing to maximize bandwidth utilization, and prefetching to hide latency. The compiler analyzes access patterns and automatically applies these transformations.

Modern accelerators feature complex memory hierarchies: registers, L1 cache, L2 cache, shared memory (scratchpad), and global memory (DRAM or HBM). Effective optimization requires orchestrating data movement across these levels to minimize expensive global memory accesses.

5.5.1 Tiling and Blocking

Tiling decomposes large operations into smaller blocks that fit in cache or shared memory. This improves temporal locality—reusing data before it's evicted—and spatial locality—accessing contiguous memory.

// Matrix multiplication with hierarchical tiling
// Original: C[M,N] = A[M,K] * B[K,N]

// Level 1: Tile for L2 cache (256 KB)
for (int mm = 0; mm < M; mm += TILE_M1) {
  for (int nn = 0; nn < N; nn += TILE_N1) {
    for (int kk = 0; kk < K; kk += TILE_K1) {

      // Level 2: Tile for L1 cache (32 KB)
      for (int m = mm; m < min(mm+TILE_M1, M); m += TILE_M2) {
        for (int n = nn; n < min(nn+TILE_N1, N); n += TILE_N2) {
          for (int k = kk; k < min(kk+TILE_K1, K); k += TILE_K2) {

            // Level 3: Register blocking
            for (int mr = m; mr < min(m+TILE_M2, M); mr += REG_M) {
              for (int nr = n; nr < min(n+TILE_N2, N); nr += REG_N) {
                // Innermost computation stays in registers
                matmul_micro_kernel(A, B, C, mr, nr, k, REG_M, REG_N, TILE_K2);
              }
            }
          }
        }
      }
    }
  }
}

// Compiler automatically determines optimal tile sizes based on:
// - Cache sizes (L1: 32KB, L2: 256KB, L3: 8MB)
// - Register file size
// - Problem dimensions
// - Data types

5.5.2 Memory Coalescing

GPUs and many NPUs achieve peak bandwidth only with coalesced memory accesses—consecutive threads accessing consecutive addresses. Compilers transform layouts and access patterns to maximize coalescing.

// Uncoalesced access pattern (poor performance)
// Each thread accesses strided memory
for (int thread_id = 0; thread_id < num_threads; thread_id++) {
    int idx = thread_id * stride;  // stride != 1, causes scattered accesses
    result[thread_id] = data[idx];
}
// Bandwidth utilization: 10-20%

// Coalesced access pattern (optimal performance)
// Consecutive threads access consecutive memory
for (int thread_id = 0; thread_id < num_threads; thread_id++) {
    int idx = thread_id;  // Consecutive access
    result[thread_id] = data[idx];
}
// Bandwidth utilization: 85-95%

// WIA-AI-011 compiler transformation
wia_memory_transform_t transform = {
    .original_layout = WIA_LAYOUT_NCHW,  // Channels strided
    .optimized_layout = WIA_LAYOUT_NHWC,  // Channels coalesced
    .reason = "Improve coalescing for element-wise operations"
};

wia_apply_layout_transform(ir, &transform);

5.5.3 Prefetching

Prefetching loads data into cache before it's needed, hiding memory latency behind computation. Software prefetching or hardware-assisted prefetching can dramatically improve performance for predictable access patterns.

// Manual prefetching in generated code
for (int i = 0; i < N; i += BLOCK_SIZE) {
    // Prefetch next iteration's data
    if (i + BLOCK_SIZE < N) {
        wia_prefetch(&A[i + BLOCK_SIZE], BLOCK_SIZE, WIA_PREFETCH_L1);
        wia_prefetch(&B[i + BLOCK_SIZE], BLOCK_SIZE, WIA_PREFETCH_L1);
    }

    // Compute current iteration while next data loads
    compute_block(&A[i], &B[i], &C[i], BLOCK_SIZE);
}

// Prefetch distance tuning
wia_prefetch_config_t pf_config = {
    .prefetch_distance = 2,  // Prefetch 2 iterations ahead
    .target_cache_level = WIA_CACHE_L2,
    .prefetch_hint = WIA_HINT_TEMPORAL  // or WIA_HINT_NON_TEMPORAL
};

5.6 Instruction-Level Parallelism

Modern accelerators execute multiple instructions concurrently through pipelining and superscalar execution. Loop unrolling exposes more parallelism for instruction schedulers. Software pipelining overlaps computation from different loop iterations. The compiler balances these techniques against register pressure and code size.

5.6.1 Loop Unrolling

Unrolling duplicates loop bodies, reducing branch overhead and exposing independent operations for parallel execution.

// Original loop
for (int i = 0; i < N; i++) {
    C[i] = A[i] + B[i];
}

// Unrolled by factor of 4
for (int i = 0; i < N; i += 4) {
    C[i+0] = A[i+0] + B[i+0];
    C[i+1] = A[i+1] + B[i+1];
    C[i+2] = A[i+2] + B[i+2];
    C[i+3] = A[i+3] + B[i+3];
}

// Benefits:
// - 75% fewer branch instructions
// - 4 independent additions can execute in parallel
// - Better instruction-level parallelism (ILP)
// - Enables vectorization

// WIA compiler automatic unrolling
wia_loop_transform_t unroll = {
    .type = WIA_TRANSFORM_UNROLL,
    .factor = 8,  // Compiler chooses factor based on register availability
    .allow_partial = true  // Handle non-multiple loop counts
};

5.6.2 Software Pipelining

Software pipelining overlaps iterations by reordering instructions, hiding latency within throughput-oriented execution.

// Original loop with dependencies
for (int i = 0; i < N; i++) {
    load = memory[i];          // 100 cycles latency
    compute = process(load);   // 10 cycles
    memory[i] = compute;       // 100 cycles latency
}
// Total per iteration: 100 + 10 + 100 = 210 cycles

// Software pipelined version
load0 = memory[0];
load1 = memory[1];
for (int i = 0; i < N-2; i++) {
    load2 = memory[i+2];      // Load iteration i+2
    compute0 = process(load0); // Process iteration i
    memory[i] = compute0;      // Store iteration i-1

    // Rotate registers
    load0 = load1;
    load1 = load2;
}
// Overlapped execution, ~10 cycles per iteration after startup

5.7 Quantization and Mixed Precision

Lower precision (INT8, FP16) enables faster computation and reduced memory usage. The compiler must insert quantization/dequantization operations correctly, choose appropriate precision for each operation, and maintain accuracy. WIA-AI-011 supports both post-training quantization (PTQ) and quantization-aware training (QAT).

5.7.1 Post-Training Quantization

PTQ converts a trained FP32 model to lower precision without retraining. This involves calibration to determine quantization scales and zero points.

// Post-training quantization workflow
wia_quantization_config_t quant_cfg = {
    .mode = WIA_QUANT_MODE_PTQ,
    .weight_precision = WIA_INT8,
    .activation_precision = WIA_INT8,
    .bias_precision = WIA_INT32,
    .calibration_method = WIA_CALIB_MINMAX,  // or WIA_CALIB_ENTROPY
    .num_calibration_batches = 100
};

// Calibration phase
wia_quantizer_t* quantizer = wia_create_quantizer(model, &quant_cfg);

for (int i = 0; i < 100; i++) {
    batch = get_calibration_batch(i);
    wia_quantizer_calibrate(quantizer, batch);
}

// Compute optimal scales and zero points
wia_quantizer_finalize(quantizer);

// Generate quantized model
wia_model_t* quantized_model = wia_quantizer_get_model(quantizer);

// Accuracy verification
float fp32_accuracy = evaluate_model(original_model, test_set);
float int8_accuracy = evaluate_model(quantized_model, test_set);
printf("Accuracy: FP32=%.2f%%, INT8=%.2f%% (delta=%.2f%%)\n",
       fp32_accuracy, int8_accuracy, fp32_accuracy - int8_accuracy);

5.7.2 Quantization-Aware Training

QAT inserts fake quantization operations during training, allowing the model to adapt to quantization errors.

// Quantization-aware training
wia_qat_config_t qat_cfg = {
    .mode = WIA_QUANT_MODE_QAT,
    .weight_precision = WIA_INT8,
    .activation_precision = WIA_INT8,
    .quantize_layers = WIA_QUANTIZE_ALL,  // or selective
    .freeze_bn_stats_after_epoch = 5
};

// Insert fake quantization nodes
wia_model_t* qat_model = wia_prepare_qat_model(original_model, &qat_cfg);

// Train with fake quantization
for (int epoch = 0; epoch < num_epochs; epoch++) {
    for (batch in training_data) {
        // Forward pass simulates quantized inference
        loss = wia_qat_forward(qat_model, batch);

        // Backward pass updates weights accounting for quantization
        wia_qat_backward(qat_model, loss);
        wia_qat_optimizer_step(qat_model);
    }
}

// Convert to actual quantized model
wia_model_t* final_quantized = wia_qat_finalize(qat_model);

5.7.3 Mixed Precision Strategies

Not all operations require the same precision. Mixed precision uses lower precision where possible while maintaining higher precision for sensitive operations.

// Mixed precision compilation
wia_precision_config_t precision = {
    .default_precision = WIA_FLOAT16,
    .accumulator_precision = WIA_FLOAT32,  // Higher precision for accumulation
    .weight_precision = WIA_INT8,
    .activation_precision = WIA_FLOAT16,

    // Per-layer overrides
    .layer_overrides = {
        {"layer_norm_*", WIA_FLOAT32},  // Layer norm sensitive to precision
        {"softmax_*", WIA_FLOAT32},     // Softmax needs FP32 for stability
        {"embedding_*", WIA_FLOAT16},   // Embeddings can use FP16
        {"matmul_*", WIA_INT8}          // Matrix multiplies use INT8
    },
    .num_overrides = 4
};

wia_compile_with_precision(kernel_spec, &precision, &kernel);

// Automatic precision selection
wia_auto_mixed_precision_config_t amp_cfg = {
    .target_accuracy_loss = 0.005,  // Max 0.5% accuracy loss
    .optimization_objective = WIA_MINIMIZE_LATENCY
};

wia_model_t* amp_model = wia_auto_mixed_precision(model, &_cfg,
                                                    validation_set);

5.8 Backend Code Generation

The backend translates optimized IR into device-specific assembly or machine code. Different accelerators require different code generation strategies. GPUs use SIMT (Single Instruction Multiple Thread), TPUs use SIMD systolic arrays, and NPUs may use specialized instruction sets. The backend must leverage each architecture's unique features.

5.8.1 Target-Specific Backends

WIA-AI-011 supports multiple backend targets, each with specialized code generators.

// GPU backend (SIMT architecture)
wia_backend_config_t gpu_cfg = {
    .target = WIA_TARGET_GPU_NVIDIA,
    .arch = WIA_ARCH_AMPERE,
    .sm_version = 86,  // Compute capability 8.6

    .thread_block_size = 256,
    .max_registers_per_thread = 255,
    .shared_memory_size = 49152,

    .use_tensor_cores = true,
    .use_async_copy = true,
    .use_cooperative_groups = true
};

wia_compiled_kernel_t* gpu_kernel = wia_generate_gpu_code(ir, &gpu_cfg);

// NPU backend (Spatial architecture)
wia_backend_config_t npu_cfg = {
    .target = WIA_TARGET_NPU_CUSTOM,
    .arch = WIA_ARCH_SPATIAL_ARRAY,

    .array_dimensions = {128, 128},  // 128x128 processing elements
    .local_memory_per_pe = 1024,     // bytes per PE
    .systolic_mode = WIA_SYSTOLIC_OUTPUT,

    .dataflow = WIA_DATAFLOW_WEIGHT_STATIONARY,
    .pipeline_depth = 8
};

wia_compiled_kernel_t* npu_kernel = wia_generate_npu_code(ir, &npu_cfg);

// TPU backend (Systolic array)
wia_backend_config_t tpu_cfg = {
    .target = WIA_TARGET_TPU_V4,
    .arch = WIA_ARCH_SYSTOLIC,

    .matrix_unit_size = 128,
    .vector_unit_lanes = 128,

    .hbm_bandwidth_gb_s = 1200,
    .peak_tflops_bf16 = 275
};

wia_compiled_kernel_t* tpu_kernel = wia_generate_tpu_code(ir, &tpu_cfg);

5.8.2 Instruction Selection and Scheduling

The backend selects appropriate instructions and schedules them to maximize throughput while respecting dependencies and resource constraints.

// Example generated assembly for GPU
// Matrix multiplication C = A * B using Tensor Cores

KERNEL_ENTRY matmul_tensorcore:
    // Load tile of A into shared memory
    LDGSTS.E.128 [sharedA], [globalA + tid*16]

    // Load tile of B into shared memory
    LDGSTS.E.128 [sharedB], [globalB + tid*16]

    // Synchronize threads
    BAR.SYNC 0

    // Tensor Core WMMA instructions
    WMMA.LOAD.A.SYNC a_frag, [sharedA], 16
    WMMA.LOAD.B.SYNC b_frag, [sharedB], 16
    WMMA.MMA.SYNC c_frag, a_frag, b_frag, c_frag

    // Store result
    BAR.SYNC 0
    WMMA.STORE.SYNC [sharedC], c_frag, 16
    STGSTS.E.128 [globalC + tid*16], [sharedC]

    EXIT

// Instruction scheduling optimizations:
// - Interleave memory loads with computation
// - Hide memory latency with arithmetic
// - Balance instruction mix for maximum throughput

5.9 JIT Compilation and Caching

Just-in-time (JIT) compilation enables runtime specialization based on actual input shapes and properties. However, compilation overhead can dominate for small operations. WIA-AI-011 implements persistent caching of compiled kernels, indexed by operation signature, to amortize compilation costs across invocations.

// JIT compile with caching
wia_jit_config_t jit_cfg = {
    .cache_dir = "/var/cache/wia-kernels",
    .cache_size_mb = 1024,
    .specialization_level = WIA_SPECIALIZE_SHAPES,
    .enable_persistent_cache = true,
    .cache_eviction_policy = WIA_EVICT_LRU
};

// First invocation: compiles and caches
wia_kernel_t* kernel1 = wia_jit_compile(ctx, operation, &jit_cfg);
// Compilation time: 250ms

// Second invocation with same shape: cache hit
wia_kernel_t* kernel2 = wia_jit_compile(ctx, operation, &jit_cfg);
// Compilation time: <1ms (cached)

// Different shape: new compilation
operation.input_shape = {2048, 512};
wia_kernel_t* kernel3 = wia_jit_compile(ctx, operation, &jit_cfg);
// Compilation time: 300ms (larger kernel)

// Cache statistics
wia_cache_stats_t stats;
wia_get_cache_stats(&jit_cfg, &stats);
printf("Cache hits: %d, misses: %d, hit rate: %.1f%%\n",
       stats.hits, stats.misses,
       100.0 * stats.hits / (stats.hits + stats.misses));

5.9.1 Adaptive Compilation

Adaptive compilation uses runtime profiling to recompile hot code paths with aggressive optimizations.

// Adaptive JIT compilation
wia_adaptive_jit_config_t adaptive_cfg = {
    .initial_optimization_level = WIA_OPT_LEVEL_1,  // Fast compilation
    .hot_threshold_invocations = 100,
    .hot_optimization_level = WIA_OPT_LEVEL_3,      // Aggressive optimization
    .enable_profiling = true
};

// Initially compiles with fast settings
wia_kernel_t* kernel = wia_adaptive_jit_compile(ctx, op, &adaptive_cfg);

// After 100 invocations, automatically recompiles with aggressive optimization
// User code unchanged, optimization happens transparently
for (int i = 0; i < 1000; i++) {
    wia_execute_kernel(ctx, kernel, inputs, outputs);
}

// At iteration 100:
// [WIA JIT] Detected hot kernel, recompiling with O3...
// [WIA JIT] Recompilation improved performance by 1.8x

5.10 Performance Profiling and Analysis

Understanding where optimization efforts should focus requires detailed profiling. WIA-AI-011 compilers generate instrumented code that tracks kernel execution time, memory bandwidth, compute utilization, and cache metrics. This profiling data guides further optimization iterations.

// Enable compiler-generated profiling
wia_compiler_profiling_t prof = {
    .enable_kernel_timing = true,
    .enable_memory_profiling = true,
    .enable_hardware_counters = true,
    .instrumentation_level = WIA_INSTRUMENT_DETAILED
};

wia_compiled_module_t* profiled_module =
    wia_compile_with_profiling(model, &prof);

// Execute and collect profile data
wia_execute_module(ctx, profiled_module, inputs, outputs);

// Analyze profiling results
wia_profile_report_t* report = wia_get_profile_report(profiled_module);

printf("Kernel Performance Analysis:\n");
for (int i = 0; i < report->num_kernels; i++) {
    wia_kernel_profile_t* kp = &report->kernels[i];
    printf("  %s:\n", kp->name);
    printf("    Time: %.3f ms\n", kp->execution_time_ms);
    printf("    GFLOPS: %.1f\n", kp->gflops);
    printf("    Memory BW: %.1f GB/s\n", kp->memory_bandwidth_gbs);
    printf("    Compute utilization: %.1f%%\n", kp->compute_utilization);
    printf("    Bottleneck: %s\n", kp->bottleneck);  // "compute" or "memory"
}

// Optimization suggestions
printf("\nOptimization Suggestions:\n");
for (int i = 0; i < report->num_suggestions; i++) {
    printf("  - %s\n", report->suggestions[i]);
}