//! Kernel compilation and optimization for hardware accelerators

use crate::error::Result;
use crate::hardware::AcceleratorType;
use std::collections::HashMap;
/// Optimization level for kernel compilation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum OptimizationLevel {
    /// No optimization
    O0,
    /// Basic optimization
    O1,
    /// Standard optimization
    O2,
    /// Aggressive optimization
    O3,
    /// Size optimization
    Os,
}
/// Kernel compiler for different hardware targets
pub struct KernelCompiler {
    optimization_level: OptimizationLevel,
    cache: HashMap<String, CompiledKernel>,
impl KernelCompiler {
    /// Create a new kernel compiler
    pub fn new(_optimizationlevel: OptimizationLevel) -> Self {
        Self {
            optimization_level,
            cache: HashMap::new(),
        }
    }
    /// Compile a kernel for a specific target
    pub fn compile(
        &mut self,
        name: &str,
        source: &str,
        target: CompilationTarget,
    ) -> Result<CompiledKernel> {
        // Check cache
        let cache_key = format!("{}_{}_{:?}", name, target.as_str(), self.optimization_level);
        if let Some(compiled) = self.cache.get(&cache_key) {
            return Ok(compiled.clone());
        // Compile based on target
        let compiled = match target {
            CompilationTarget::CUDA => self.compile_cuda(name, source)?,
            CompilationTarget::OpenCL => self.compile_opencl(name, source)?,
            CompilationTarget::Metal => self.compile_metal(name, source)?,
            CompilationTarget::SPIRV => self.compile_spirv(name, source)?,
            CompilationTarget::CPU => self.compile_cpu(name, source)?,
        };
        // Cache the result
        self.cache.insert(cache_key, compiled.clone());
        Ok(compiled)
    /// Compile CUDA kernel
    fn compile_cuda(&self, name: &str, source: &str) -> Result<CompiledKernel> {
        let mut options = CudaCompileOptions::default();
        // Set optimization flags based on level
        match self.optimization_level {
            OptimizationLevel::O0 => {
                options.add_flag("-O0");
            }
            OptimizationLevel::O1 => {
                options.add_flag("-O1");
            OptimizationLevel::O2 => {
                options.add_flag("-O2");
                options.add_flag("-use_fast_math");
            OptimizationLevel::O3 => {
                options.add_flag("-O3");
                options.add_flag("-maxrregcount=64");
        // Simulate compilation
        let ptx = self.nvcc_compile(source, &options)?;
        Ok(CompiledKernel {
            name: name.to_string(),
            binary: ptx.into_bytes(),
            target: CompilationTarget::CUDA,
            metadata: KernelMetadata {
                shared_memory: 0,
                registers_per_thread: 32,
                threads_per_block: 256,
                optimization_hints: vec![],
            },
        })
    /// Compile OpenCL kernel
    fn compile_opencl(&self, name: &str, source: &str) -> Result<CompiledKernel> {
        // OpenCL compilation options
        let mut options = String::new();
            OptimizationLevel::O0 => options.push_str("-cl-opt-disable "),
            OptimizationLevel::O1 => {}
            OptimizationLevel::O2 => options.push_str("-cl-fast-relaxed-math "),
                options.push_str("-cl-fast-relaxed-math ");
                options.push_str("-cl-mad-enable ");
            binary: source.as_bytes().to_vec(),
            target: CompilationTarget::OpenCL,
            metadata: KernelMetadata::default(),
    /// Compile Metal kernel
    fn compile_metal(&self, name: &str, source: &str) -> Result<CompiledKernel> {
        // Metal compilation would use metallib
            target: CompilationTarget::Metal,
    /// Compile to SPIR-V
    fn compile_spirv(&self, name: &str, source: &str) -> Result<CompiledKernel> {
        // SPIR-V compilation would use spirv-tools
            binary: vec![0x07, 0x23, 0x02, 0x03], // SPIR-V magic number
            target: CompilationTarget::SPIRV,
    /// Compile CPU kernel (using LLVM or similar)
    fn compile_cpu(&self, name: &str, source: &str) -> Result<CompiledKernel> {
            target: CompilationTarget::CPU,
    /// Simulate NVCC compilation
    fn nvcc_compile(&self, source: &str, options: &CudaCompileOptions) -> Result<String> {
        // This would actually invoke nvcc
        let mut ptx = String::from("// Generated PTX\n");
        ptx.push_str(".version 7.0\n");
        ptx.push_str(".target sm_70\n");
        ptx.push_str(&format!("// Source: {} lines\n", source.lines().count()));
        ptx.push_str(&format!("// Options: {}\n", options.to_string()));
        Ok(ptx)
    /// Optimize kernel source
    pub fn optimize_kernel(&self, source: &str, target: CompilationTarget) -> Result<String> {
        let mut optimized = source.to_string();
        // Apply target-specific optimizations
        match target {
            CompilationTarget::CUDA => {
                optimized = self.optimize_cuda_kernel(&optimized)?;
            CompilationTarget::OpenCL => {
                optimized = self.optimize_opencl_kernel(&optimized)?;
            _ => {}
        Ok(optimized)
    /// CUDA-specific optimizations
    fn optimize_cuda_kernel(&self, source: &str) -> Result<String> {
        if self.optimization_level >= OptimizationLevel::O2 {
            // Unroll loops
            optimized = optimized.replace("#pragma unroll", "#pragma unroll 4");
            // Use fast math intrinsics
            optimized = optimized.replace("expf(", "__expf(");
            optimized = optimized.replace("logf(", "__logf(");
            optimized = optimized.replace("sqrtf(", "__fsqrt_rn(");
    /// OpenCL-specific optimizations
    fn optimize_opencl_kernel(&self, source: &str) -> Result<String> {
            // Use native functions
            optimized = optimized.replace("exp(", "native_exp(");
            optimized = optimized.replace("log(", "native_log(");
            optimized = optimized.replace("sqrt(", "native_sqrt(");
/// Compilation target
pub enum CompilationTarget {
    CUDA,
    OpenCL,
    Metal,
    SPIRV,
    CPU,
impl CompilationTarget {
    /// Get string representation
    pub fn as_str(&self) -> &'static str {
        match self {
            CompilationTarget::CUDA => "cuda",
            CompilationTarget::OpenCL => "opencl",
            CompilationTarget::Metal => "metal",
            CompilationTarget::SPIRV => "spirv",
            CompilationTarget::CPU => "cpu",
    /// From accelerator type
    pub fn from_accelerator(_acctype: AcceleratorType) -> Self {
        match _acc_type {
            AcceleratorType::CUDA => CompilationTarget::CUDA,
            AcceleratorType::ROCm => CompilationTarget::OpenCL,
            AcceleratorType::OneAPI => CompilationTarget::SPIRV,
            AcceleratorType::Metal => CompilationTarget::Metal_ => CompilationTarget::CPU,
/// Compiled kernel representation
#[derive(Clone)]
pub struct CompiledKernel {
    pub name: String,
    pub binary: Vec<u8>,
    pub target: CompilationTarget,
    pub metadata: KernelMetadata,
/// Kernel metadata
#[derive(Clone, Default)]
pub struct KernelMetadata {
    pub shared_memory: usize,
    pub registers_per_thread: u32,
    pub threads_per_block: u32,
    pub optimization_hints: Vec<String>,
/// CUDA compilation options
#[derive(Default)]
struct CudaCompileOptions {
    flags: Vec<String>,
impl CudaCompileOptions {
    fn add_flag(&mut self, flag: &str) {
        self.flags.push(flag.to_string());
    fn to_string(&self) -> String {
        self.flags.join(" ")
/// Kernel template generator
pub struct KernelTemplateGenerator;
impl KernelTemplateGenerator {
    /// Generate matrix multiplication kernel
    pub fn generate_matmul(
        m: usize,
        n: usize,
        k: usize,
        tile_size: usize,
    ) -> String {
            CompilationTarget::CUDA => Self::cuda_matmul_template(m, n, k, tile_size),
            CompilationTarget::OpenCL => Self::opencl_matmul_template(m, n, k, tile_size, _ => String::new(),
    /// CUDA matrix multiplication template
    fn cuda_matmul_template(m: usize, n: usize, k: usize, tilesize: usize) -> String {
        format!(
            r#"
__global__ void matmul_kernel(
    const float* __restrict__ A,
    const float* __restrict__ B,
    float* __restrict__ C,
    int M, int N, int K
) {{
    const int TILE_SIZE = {};
    
    __shared__ float As[TILE_SIZE][TILE_SIZE];
    __shared__ float Bs[TILE_SIZE][TILE_SIZE];
    int bx = blockIdx.x;
    int by = blockIdx.y;
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    int row = by * TILE_SIZE + ty;
    int col = bx * TILE_SIZE + tx;
    float sum = 0.0f;
    for (int tile = 0; tile < (K + TILE_SIZE - 1) / TILE_SIZE; ++tile) {{
        if (row < M && tile * TILE_SIZE + tx < K)
            As[ty][tx] = A[row * K + tile * TILE_SIZE + tx];
        else
            As[ty][tx] = 0.0f;
            
        if (col < N && tile * TILE_SIZE + ty < K)
            Bs[ty][tx] = B[(tile * TILE_SIZE + ty) * N + col];
            Bs[ty][tx] = 0.0f;
        __syncthreads();
        
        #pragma unroll
        for (int k = 0; k < TILE_SIZE; ++k) {{
            sum += As[ty][k] * Bs[k][tx];
        }}
    }}
    if (row < M && col < N) {{
        C[row * N + col] = sum;
}}
"#,
            tile_size
        )
    /// OpenCL matrix multiplication template
    fn opencl_matmul_template(m: usize, n: usize, k: usize, tilesize: usize) -> String {
__kernel void matmul_kernel(
    __global const float* A__global const float* B__global float* C__local float As[TILE_SIZE][TILE_SIZE];
    __local float Bs[TILE_SIZE][TILE_SIZE];
    int gx = get_global_id(0);
    int gy = get_global_id(1);
    int lx = get_local_id(0);
    int ly = get_local_id(1);
        // Load tiles into local memory
        As[ly][lx] = (gy < M && tile * TILE_SIZE + lx < K) ? 
            A[gy * K + tile * TILE_SIZE + lx] : 0.0f;
        Bs[ly][lx] = (gx < N && tile * TILE_SIZE + ly < K) ?
            B[(tile * TILE_SIZE + ly) * N + gx] : 0.0f;
        barrier(CLK_LOCAL_MEM_FENCE);
            sum += As[ly][k] * Bs[k][lx];
    if (gy < M && gx < N) {{
        C[gy * N + gx] = sum;
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_kernel_compiler() {
        let mut compiler = KernelCompiler::new(OptimizationLevel::O2);
        let source = "__global__ void test() {}";
        let compiled = compiler
            .compile("test", source, CompilationTarget::CUDA)
            .expect("Operation failed");
        assert_eq!(compiled.name, "test");
        assert_eq!(compiled.target, CompilationTarget::CUDA);
        assert!(!compiled.binary.is_empty());
    fn test_compilation_target() {
        assert_eq!(CompilationTarget::CUDA.as_str(), "cuda");
        assert_eq!(
            CompilationTarget::from_accelerator(AcceleratorType::CUDA),
            CompilationTarget::CUDA
        );
    fn test_kernel_template_generator() {
        let cuda_kernel =
            KernelTemplateGenerator::generate_matmul(CompilationTarget::CUDA, 128, 128, 128, 16);
        assert!(cuda_kernel.contains("__global__"));
        assert!(cuda_kernel.contains("matmul_kernel"));
        assert!(cuda_kernel.contains("TILE_SIZE = 16"));
