oxirs_vec/hnsw/

gpu.rs

//! GPU acceleration for HNSW operations

#[cfg(feature = "gpu")]
use crate::gpu::GpuAccelerator;
use crate::hnsw::HnswIndex;
use crate::Vector;
use anyhow::Result;
use std::sync::Arc;

/// GPU performance statistics
#[derive(Debug, Clone)]
pub struct GpuPerformanceStats {
    pub gpu_memory_used: usize,
    pub kernel_execution_time: f64,
    pub memory_transfer_time: f64,
    pub throughput_vectors_per_second: f64,
}

#[cfg(feature = "gpu")]
impl HnswIndex {
    /// GPU-accelerated batch distance calculation
    pub fn gpu_batch_distance_calculation(
        &self,
        query: &Vector,
        candidates: &[usize],
    ) -> Result<Vec<f32>> {
        if candidates.len() < self.config().gpu_batch_threshold {
            // Fall back to CPU for small batches
            return self.cpu_batch_distance_calculation(query, candidates);
        }

        if let Some(accelerator) = self.gpu_accelerator() {
            self.single_gpu_distance_calculation(accelerator, query, candidates)
        } else if !self.multi_gpu_accelerators().is_empty() {
            self.multi_gpu_distance_calculation(query, candidates)
        } else {
            // Fallback to CPU
            self.cpu_batch_distance_calculation(query, candidates)
        }
    }

    /// Single GPU distance calculation
    pub fn single_gpu_distance_calculation(
        &self,
        _accelerator: &Arc<GpuAccelerator>,
        query: &Vector,
        candidates: &[usize],
    ) -> Result<Vec<f32>> {
        // Placeholder for single GPU implementation
        // Real implementation would:
        // 1. Transfer query vector to GPU
        // 2. Transfer candidate vectors to GPU
        // 3. Launch CUDA kernels for distance calculation
        // 4. Transfer results back to CPU

        // For now, fall back to CPU
        self.cpu_batch_distance_calculation(query, candidates)
    }

    /// Multi-GPU distance calculation with load balancing
    pub fn multi_gpu_distance_calculation(
        &self,
        query: &Vector,
        candidates: &[usize],
    ) -> Result<Vec<f32>> {
        if self.multi_gpu_accelerators().is_empty() {
            return self.cpu_batch_distance_calculation(query, candidates);
        }

        // Placeholder for multi-GPU implementation
        // Real implementation would:
        // 1. Partition candidates across GPUs
        // 2. Distribute work based on GPU capabilities
        // 3. Launch parallel computations
        // 4. Collect and merge results

        // For now, fall back to CPU
        self.cpu_batch_distance_calculation(query, candidates)
    }

    /// GPU-accelerated search with CUDA kernels
    pub fn gpu_search(&self, query: &Vector, k: usize) -> Result<Vec<(String, f32)>> {
        // Implementation of GPU-accelerated HNSW search

        if self.nodes().is_empty() || self.entry_point().is_none() {
            return Ok(Vec::new());
        }

        // Check if GPU acceleration is available
        if !self.is_gpu_enabled() {
            // Fallback to CPU search
            return self.search_knn(query, k);
        }

        // For large search operations, use GPU acceleration
        if k >= self.config().gpu_batch_threshold && self.nodes().len() >= 1000 {
            return self.gpu_accelerated_search_large(query, k);
        }

        // For smaller operations, regular CPU search might be faster
        self.search_knn(query, k)
    }

    /// GPU-accelerated search for large datasets
    fn gpu_accelerated_search_large(&self, query: &Vector, k: usize) -> Result<Vec<(String, f32)>> {
        // Get all candidate node IDs
        let candidate_ids: Vec<usize> = (0..self.nodes().len()).collect();

        // Use GPU batch distance calculation
        let distances = self.gpu_batch_distance_calculation(query, &candidate_ids)?;

        // Combine IDs with distances and sort
        let mut id_distance_pairs: Vec<(usize, f32)> =
            candidate_ids.into_iter().zip(distances).collect();

        // Sort by distance (ascending)
        id_distance_pairs
            .sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));

        // Take top k and convert to results
        let results: Vec<(String, f32)> = id_distance_pairs
            .into_iter()
            .take(k)
            .filter_map(|(id, distance)| {
                self.nodes()
                    .get(id)
                    .map(|node| (node.uri.clone(), distance))
            })
            .collect();

        Ok(results)
    }

    /// Check if GPU acceleration is enabled and available
    pub fn is_gpu_enabled(&self) -> bool {
        self.gpu_accelerator().is_some() || !self.multi_gpu_accelerators().is_empty()
    }

    /// Get GPU performance statistics
    pub fn gpu_performance_stats(&self) -> Option<GpuPerformanceStats> {
        self.gpu_accelerator()
            .map(|accelerator| GpuPerformanceStats {
                gpu_memory_used: accelerator.get_memory_usage().unwrap_or(0),
                kernel_execution_time: 0.0, // Would be tracked in real implementation
                memory_transfer_time: 0.0,  // Would be tracked in real implementation
                throughput_vectors_per_second: 0.0, // Would be calculated in real implementation
            })
    }

    /// Warm up GPU kernels and memory
    pub fn warmup_gpu(&self) -> Result<()> {
        if !self.is_gpu_enabled() {
            return Ok(());
        }

        // Placeholder for GPU warmup
        // Real implementation would:
        // 1. Pre-allocate GPU memory
        // 2. Compile and cache kernels
        // 3. Warm up GPU clocks

        Ok(())
    }

    /// Transfer index data to GPU memory for faster access
    pub fn preload_to_gpu(&self) -> Result<()> {
        if !self.is_gpu_enabled() {
            return Ok(());
        }

        // Placeholder for GPU data preloading
        // Real implementation would:
        // 1. Transfer all vectors to GPU memory
        // 2. Create optimized GPU data structures
        // 3. Cache frequently accessed data

        Ok(())
    }
}

#[cfg(not(feature = "gpu"))]
impl HnswIndex {
    /// Stub implementation when GPU feature is disabled
    pub fn gpu_batch_distance_calculation(
        &self,
        query: &Vector,
        candidates: &[usize],
    ) -> Result<Vec<f32>> {
        self.cpu_batch_distance_calculation(query, candidates)
    }
}