scirs2-series 0.3.2

//! GPU-accelerated array implementation
//!
//! This module provides the GpuArray structure for managing data transfers
//! between CPU and GPU memory, with support for various optimization strategies.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Float;
use std::fmt::Debug;

use super::config::GpuConfig;
use super::traits::GpuAccelerated;
use crate::error::{Result, TimeSeriesError};

/// GPU-accelerated array wrapper
#[derive(Debug)]
pub struct GpuArray<F: Float + Debug> {
    /// CPU data (if available)
    cpu_data: Option<Array1<F>>,
    /// GPU data handle (placeholder for actual GPU memory)
    #[allow(dead_code)]
    gpu_handle: Option<usize>,
    /// Configuration
    #[allow(dead_code)]
    config: GpuConfig,
    /// Whether data is currently on GPU
    on_gpu: bool,
}

impl<F: Float + Debug + Clone> GpuArray<F> {
    /// Create a new GPU array from CPU data
    pub fn from_cpu(data: Array1<F>, config: GpuConfig) -> Self {
        Self {
            cpu_data: Some(data),
            gpu_handle: None,
            config,
            on_gpu: false,
        }
    }

    /// Create a new empty GPU array
    pub fn zeros(len: usize, config: GpuConfig) -> Self {
        let data = Array1::zeros(len);
        Self::from_cpu(data, config)
    }

    /// Get the length of the array
    pub fn len(&self) -> usize {
        if let Some(ref data) = self.cpu_data {
            data.len()
        } else {
            0 // Would query GPU in actual implementation
        }
    }

    /// Check if array is empty
    pub fn is_empty(&self) -> bool {
        self.len() == 0
    }

    /// Get CPU data (transfer from GPU if necessary)
    pub fn to_cpu_data(&self) -> Result<Array1<F>> {
        if let Some(ref data) = self.cpu_data {
            Ok(data.clone())
        } else {
            // In actual implementation, would transfer from GPU
            Err(TimeSeriesError::NotImplemented(
                "GPU to CPU transfer requires GPU framework dependencies".to_string(),
            ))
        }
    }
}

impl<F: Float + Debug + Clone> GpuAccelerated<F> for GpuArray<F> {
    fn to_gpu(&self, config: &GpuConfig) -> Result<Self> {
        // Simulate GPU transfer with optimized CPU implementation
        // In actual implementation, this would transfer to GPU memory
        let optimized_data = if config.use_half_precision {
            // Simulate FP16 conversion (would reduce memory usage on GPU)
            self.cpu_data.as_ref().map(|data| {
                data.mapv(|x| {
                    // Simulate half precision by reducing numerical precision
                    let fp16_sim = (x.to_f64().unwrap_or(0.0) * 1000.0).round() / 1000.0;
                    F::from(fp16_sim).unwrap_or(x)
                })
            })
        } else {
            self.cpu_data.clone()
        };

        Ok(Self {
            cpu_data: optimized_data,
            gpu_handle: Some(42), // Placeholder handle
            config: config.clone(),
            on_gpu: true, // Mark as "on GPU" (simulated)
        })
    }

    fn to_cpu(&self) -> Result<Self> {
        if !self.on_gpu {
            return Ok(Self {
                cpu_data: self.cpu_data.clone(),
                gpu_handle: None,
                config: self.config.clone(),
                on_gpu: false,
            });
        }

        // GPU to CPU transfer implementation
        // In actual GPU implementation, this would copy data from GPU memory to CPU
        let transferred_data = if let Some(ref cpu_data) = self.cpu_data {
            // For simulation, we already have CPU data available
            // In real implementation, this would use CUDA/OpenCL/Metal APIs
            Some(cpu_data.clone())
        } else {
            // In real implementation, we would query GPU memory size and transfer
            // For now, return error if no CPU fallback is available
            return Err(TimeSeriesError::NotImplemented(
                "GPU memory reconstruction not implemented without CPU fallback".to_string(),
            ));
        };

        Ok(Self {
            cpu_data: transferred_data,
            gpu_handle: None, // Release GPU handle after transfer
            config: self.config.clone(),
            on_gpu: false,
        })
    }

    fn is_on_gpu(&self) -> bool {
        self.on_gpu
    }

    fn gpu_memory_usage(&self) -> usize {
        if self.on_gpu {
            self.len() * std::mem::size_of::<F>()
        } else {
            0
        }
    }
}