oxirs-core 0.2.2

//! GPU-accelerated query operations for massive parallel processing
//!
//! This module provides GPU acceleration for RDF query operations using
//! a generic interface that can work with CUDA, OpenCL, or WebGPU backends.

#![allow(dead_code)]

use crate::model::*;
use crate::query::plan::ExecutionPlan;
use crate::OxirsError;
use std::sync::Arc;

/// GPU backend types
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum GpuBackend {
    /// NVIDIA CUDA
    Cuda,
    /// OpenCL (cross-platform)
    OpenCL,
    /// WebGPU (modern cross-platform)
    WebGPU,
    /// CPU fallback
    CpuFallback,
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GpuDevice {
    /// Device name
    pub name: String,
    /// Total memory in bytes
    pub memory_bytes: usize,
    /// Number of compute units
    pub compute_units: usize,
    /// Maximum work group size
    pub max_work_group_size: usize,
    /// Backend type
    pub backend: GpuBackend,
}

/// GPU-accelerated query executor
pub struct GpuQueryExecutor {
    /// Available GPU devices
    devices: Vec<GpuDevice>,
    /// Selected device index
    selected_device: usize,
    /// Memory pool for GPU operations
    memory_pool: Arc<GpuMemoryPool>,
}

/// GPU memory pool for efficient allocation
struct GpuMemoryPool {
    /// Total pool size
    total_size: usize,
    /// Free blocks
    free_blocks: Vec<MemoryBlock>,
    /// Allocated blocks
    allocated_blocks: Vec<MemoryBlock>,
}

/// Memory block on GPU
#[derive(Debug, Clone)]
struct MemoryBlock {
    /// Offset in pool
    offset: usize,
    /// Size in bytes
    size: usize,
    /// Whether currently allocated
    allocated: bool,
}

/// GPU kernel for triple pattern matching
#[repr(C)]
struct TripleMatchKernel {
    /// Pattern to match (encoded)
    pattern: [u32; 3],
    /// Flags for which components to match
    match_flags: u32,
    /// Output buffer offset
    output_offset: u32,
}

/// GPU-optimized triple representation
#[repr(C)]
struct GpuTriple {
    /// Subject ID
    subject_id: u32,
    /// Predicate ID  
    predicate_id: u32,
    /// Object ID
    object_id: u32,
    /// Flags and metadata
    flags: u32,
}

impl GpuQueryExecutor {
    /// Create new GPU query executor
    pub fn new() -> Result<Self, OxirsError> {
        let devices = Self::detect_devices()?;

        if devices.is_empty() {
            return Err(OxirsError::Query("No GPU devices available".to_string()));
        }

        Ok(Self {
            devices: devices.clone(),
            selected_device: 0,
            memory_pool: Arc::new(GpuMemoryPool::new(
                devices[0].memory_bytes / 2, // Use half of GPU memory
            )),
        })
    }

    /// Detect available GPU devices
    fn detect_devices() -> Result<Vec<GpuDevice>, OxirsError> {
        let mut devices = Vec::new();

        // Try CUDA first
        if let Some(cuda_devices) = Self::detect_cuda_devices() {
            devices.extend(cuda_devices);
        }

        // Try OpenCL
        if let Some(opencl_devices) = Self::detect_opencl_devices() {
            devices.extend(opencl_devices);
        }

        // Try WebGPU
        if let Some(webgpu_devices) = Self::detect_webgpu_devices() {
            devices.extend(webgpu_devices);
        }

        // Always add CPU fallback
        devices.push(GpuDevice {
            name: "CPU Fallback".to_string(),
            memory_bytes: 8 * 1024 * 1024 * 1024, // 8GB
            compute_units: std::thread::available_parallelism()
                .map(|p| p.get())
                .unwrap_or(1),
            max_work_group_size: 1024,
            backend: GpuBackend::CpuFallback,
        });

        Ok(devices)
    }

    /// Detect CUDA devices
    fn detect_cuda_devices() -> Option<Vec<GpuDevice>> {
        // Placeholder - would use CUDA API
        None
    }

    /// Detect OpenCL devices
    fn detect_opencl_devices() -> Option<Vec<GpuDevice>> {
        // Placeholder - would use OpenCL API
        None
    }

    /// Detect WebGPU devices
    fn detect_webgpu_devices() -> Option<Vec<GpuDevice>> {
        // Placeholder - would use WebGPU API
        None
    }

    /// Execute query plan on GPU
    pub fn execute_plan(
        &self,
        plan: &ExecutionPlan,
        data: &GpuData,
    ) -> Result<GpuResults, OxirsError> {
        match self.devices[self.selected_device].backend {
            GpuBackend::Cuda => self.execute_cuda(plan, data),
            GpuBackend::OpenCL => self.execute_opencl(plan, data),
            GpuBackend::WebGPU => self.execute_webgpu(plan, data),
            GpuBackend::CpuFallback => {
                #[cfg(feature = "parallel")]
                return self.execute_cpu_parallel(plan, data);
                #[cfg(not(feature = "parallel"))]
                return Err(OxirsError::Query(
                    "CPU fallback requires 'parallel' feature".to_string(),
                ));
            }
        }
    }

    /// Execute on CUDA
    fn execute_cuda(
        &self,
        _plan: &ExecutionPlan,
        _data: &GpuData,
    ) -> Result<GpuResults, OxirsError> {
        Err(OxirsError::Query("CUDA not implemented".to_string()))
    }

    /// Execute on OpenCL
    fn execute_opencl(
        &self,
        _plan: &ExecutionPlan,
        _data: &GpuData,
    ) -> Result<GpuResults, OxirsError> {
        Err(OxirsError::Query("OpenCL not implemented".to_string()))
    }

    /// Execute on WebGPU
    fn execute_webgpu(
        &self,
        _plan: &ExecutionPlan,
        _data: &GpuData,
    ) -> Result<GpuResults, OxirsError> {
        Err(OxirsError::Query("WebGPU not implemented".to_string()))
    }

    /// Execute with CPU parallel fallback
    #[cfg(feature = "parallel")]
    fn execute_cpu_parallel(
        &self,
        plan: &ExecutionPlan,
        data: &GpuData,
    ) -> Result<GpuResults, OxirsError> {
        use rayon::prelude::*;

        match plan {
            ExecutionPlan::TripleScan { pattern } => {
                // Parallel scan using rayon
                let results: Vec<usize> = data
                    .triples
                    .par_iter()
                    .enumerate()
                    .filter_map(|(idx, triple)| {
                        if self.triple_matches_pattern(triple, pattern) {
                            Some(idx)
                        } else {
                            None
                        }
                    })
                    .collect();

                Ok(GpuResults {
                    indices: results,
                    execution_time_ms: 0.0, // Would measure
                })
            }
            _ => Err(OxirsError::Query(
                "GPU execution not supported for this plan type".to_string(),
            )),
        }
    }

    /// Check if triple matches pattern
    fn triple_matches_pattern(
        &self,
        _triple: &GpuTriple,
        _pattern: &crate::model::pattern::TriplePattern,
    ) -> bool {
        // Simplified matching - would use actual term resolution
        true
    }

    /// Upload data to GPU
    pub fn upload_data(&self, triples: &[Triple]) -> Result<GpuData, OxirsError> {
        // Convert triples to GPU format
        let gpu_triples: Vec<GpuTriple> = triples
            .iter()
            .map(|t| self.triple_to_gpu(t))
            .collect::<Result<Vec<_>, _>>()?;

        // Allocate GPU memory
        let size = gpu_triples.len() * std::mem::size_of::<GpuTriple>();
        let block = self.memory_pool.allocate(size)?;

        // Would copy to actual GPU memory
        Ok(GpuData {
            triples: gpu_triples,
            memory_block: block,
        })
    }

    /// Convert triple to GPU format
    fn triple_to_gpu(&self, _triple: &Triple) -> Result<GpuTriple, OxirsError> {
        // Would map terms to IDs
        Ok(GpuTriple {
            subject_id: 1,
            predicate_id: 2,
            object_id: 3,
            flags: 0,
        })
    }
}

/// GPU data container
pub struct GpuData {
    /// Triples in GPU format
    triples: Vec<GpuTriple>,
    /// Memory block allocation
    memory_block: MemoryBlock,
}

/// GPU query results
pub struct GpuResults {
    /// Matching triple indices
    pub indices: Vec<usize>,
    /// Execution time in milliseconds
    pub execution_time_ms: f32,
}

impl GpuMemoryPool {
    fn new(size: usize) -> Self {
        Self {
            total_size: size,
            free_blocks: vec![MemoryBlock {
                offset: 0,
                size,
                allocated: false,
            }],
            allocated_blocks: Vec::new(),
        }
    }

    fn allocate(&self, size: usize) -> Result<MemoryBlock, OxirsError> {
        // Simple first-fit allocation
        for block in &self.free_blocks {
            if !block.allocated && block.size >= size {
                return Ok(MemoryBlock {
                    offset: block.offset,
                    size,
                    allocated: true,
                });
            }
        }

        Err(OxirsError::Store("GPU memory exhausted".to_string()))
    }
}

/// GPU kernel implementations (OpenCL/CUDA style)
pub mod kernels {
    /// Triple pattern matching kernel
    pub const TRIPLE_MATCH_KERNEL: &str = r#"
        __kernel void match_triples(
            __global const uint3* triples,
            __global const uint* pattern,
            __global uint* results,
            const uint num_triples,
            const uint match_flags
        ) {
            const uint gid = get_global_id(0);
            if (gid >= num_triples) return;
            
            const uint3 triple = triples[gid];
            bool matches = true;
            
            // Check subject match
            if (match_flags & 0x1) {
                matches &= (triple.x == pattern[0]);
            }
            
            // Check predicate match
            if (match_flags & 0x2) {
                matches &= (triple.y == pattern[1]);
            }
            
            // Check object match
            if (match_flags & 0x4) {
                matches &= (triple.z == pattern[2]);
            }
            
            if (matches) {
                // Atomic increment result counter and store index
                uint idx = atomic_inc(&results[0]);
                results[idx + 1] = gid;
            }
        }
    "#;

    /// Join kernel for combining results
    pub const JOIN_KERNEL: &str = r#"
        __kernel void hash_join(
            __global const uint* left_results,
            __global const uint* right_results,
            __global uint* output,
            const uint left_size,
            const uint right_size,
            const uint join_column
        ) {
            const uint gid = get_global_id(0);
            if (gid >= left_size) return;
            
            const uint left_val = left_results[gid * 3 + join_column];
            
            // Search for matches in right results
            for (uint i = 0; i < right_size; i++) {
                if (right_results[i * 3 + join_column] == left_val) {
                    // Found match, output combined result
                    uint idx = atomic_inc(&output[0]);
                    output[idx * 6 + 1] = left_results[gid * 3];
                    output[idx * 6 + 2] = left_results[gid * 3 + 1];
                    output[idx * 6 + 3] = left_results[gid * 3 + 2];
                    output[idx * 6 + 4] = right_results[i * 3];
                    output[idx * 6 + 5] = right_results[i * 3 + 1];
                    output[idx * 6 + 6] = right_results[i * 3 + 2];
                }
            }
        }
    "#;

    /// Aggregation kernel
    pub const AGGREGATE_KERNEL: &str = r#"
        __kernel void count_aggregate(
            __global const uint* input,
            __global uint* counts,
            const uint size,
            const uint group_column
        ) {
            const uint gid = get_global_id(0);
            if (gid >= size) return;
            
            const uint group_val = input[gid * 3 + group_column];
            atomic_inc(&counts[group_val]);
        }
    "#;
}

/// GPU-accelerated operations
pub trait GpuAccelerated {
    /// Check if operation can be GPU accelerated
    fn can_accelerate(&self) -> bool;

    /// Estimate GPU speedup factor
    fn estimate_speedup(&self, data_size: usize) -> f32;

    /// Convert to GPU-executable format
    fn to_gpu_operation(&self) -> Result<GpuOperation, OxirsError>;
}

/// GPU operation type
pub enum GpuOperation {
    /// Pattern matching
    PatternMatch {
        pattern: TriplePattern,
        selectivity: f32,
    },
    /// Join operation
    Join {
        left_size: usize,
        right_size: usize,
        join_type: JoinType,
    },
    /// Aggregation
    Aggregate {
        function: AggregateFunction,
        group_by: Option<Variable>,
    },
}

/// Join types for GPU
#[derive(Debug, Clone)]
pub enum JoinType {
    Hash,
    Sort,
    Index,
}

/// Aggregate functions
#[derive(Debug, Clone)]
pub enum AggregateFunction {
    Count,
    Sum,
    Min,
    Max,
    Average,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gpu_detection() {
        let devices = GpuQueryExecutor::detect_devices().expect("construction should succeed");

        // Should at least have CPU fallback
        assert!(!devices.is_empty());
        assert_eq!(
            devices
                .last()
                .expect("collection should not be empty")
                .backend,
            GpuBackend::CpuFallback
        );
    }

    #[test]
    fn test_memory_pool() {
        let pool = GpuMemoryPool::new(1024 * 1024); // 1MB

        let block = pool.allocate(1024).expect("pool operation should succeed");
        assert_eq!(block.size, 1024);
        assert!(block.allocated);
    }
}