scirs2_series/gpu_acceleration/
array.rs

1//! GPU-accelerated array implementation
2//!
3//! This module provides the GpuArray structure for managing data transfers
4//! between CPU and GPU memory, with support for various optimization strategies.
5
6use scirs2_core::ndarray::Array1;
7use scirs2_core::numeric::Float;
8use std::fmt::Debug;
9
10use super::config::GpuConfig;
11use super::traits::GpuAccelerated;
12use crate::error::{Result, TimeSeriesError};
13
14/// GPU-accelerated array wrapper
15#[derive(Debug)]
16pub struct GpuArray<F: Float + Debug> {
17    /// CPU data (if available)
18    cpu_data: Option<Array1<F>>,
19    /// GPU data handle (placeholder for actual GPU memory)
20    #[allow(dead_code)]
21    gpu_handle: Option<usize>,
22    /// Configuration
23    #[allow(dead_code)]
24    config: GpuConfig,
25    /// Whether data is currently on GPU
26    on_gpu: bool,
27}
28
29impl<F: Float + Debug + Clone> GpuArray<F> {
30    /// Create a new GPU array from CPU data
31    pub fn from_cpu(data: Array1<F>, config: GpuConfig) -> Self {
32        Self {
33            cpu_data: Some(data),
34            gpu_handle: None,
35            config,
36            on_gpu: false,
37        }
38    }
39
40    /// Create a new empty GPU array
41    pub fn zeros(len: usize, config: GpuConfig) -> Self {
42        let data = Array1::zeros(len);
43        Self::from_cpu(data, config)
44    }
45
46    /// Get the length of the array
47    pub fn len(&self) -> usize {
48        if let Some(ref data) = self.cpu_data {
49            data.len()
50        } else {
51            0 // Would query GPU in actual implementation
52        }
53    }
54
55    /// Check if array is empty
56    pub fn is_empty(&self) -> bool {
57        self.len() == 0
58    }
59
60    /// Get CPU data (transfer from GPU if necessary)
61    pub fn to_cpu_data(&self) -> Result<Array1<F>> {
62        if let Some(ref data) = self.cpu_data {
63            Ok(data.clone())
64        } else {
65            // In actual implementation, would transfer from GPU
66            Err(TimeSeriesError::NotImplemented(
67                "GPU to CPU transfer requires GPU framework dependencies".to_string(),
68            ))
69        }
70    }
71}
72
73impl<F: Float + Debug + Clone> GpuAccelerated<F> for GpuArray<F> {
74    fn to_gpu(&self, config: &GpuConfig) -> Result<Self> {
75        // Simulate GPU transfer with optimized CPU implementation
76        // In actual implementation, this would transfer to GPU memory
77        let optimized_data = if config.use_half_precision {
78            // Simulate FP16 conversion (would reduce memory usage on GPU)
79            self.cpu_data.as_ref().map(|data| {
80                data.mapv(|x| {
81                    // Simulate half precision by reducing numerical precision
82                    let fp16_sim = (x.to_f64().unwrap_or(0.0) * 1000.0).round() / 1000.0;
83                    F::from(fp16_sim).unwrap_or(x)
84                })
85            })
86        } else {
87            self.cpu_data.clone()
88        };
89
90        Ok(Self {
91            cpu_data: optimized_data,
92            gpu_handle: Some(42), // Placeholder handle
93            config: config.clone(),
94            on_gpu: true, // Mark as "on GPU" (simulated)
95        })
96    }
97
98    fn to_cpu(&self) -> Result<Self> {
99        if !self.on_gpu {
100            return Ok(Self {
101                cpu_data: self.cpu_data.clone(),
102                gpu_handle: None,
103                config: self.config.clone(),
104                on_gpu: false,
105            });
106        }
107
108        // GPU to CPU transfer implementation
109        // In actual GPU implementation, this would copy data from GPU memory to CPU
110        let transferred_data = if let Some(ref cpu_data) = self.cpu_data {
111            // For simulation, we already have CPU data available
112            // In real implementation, this would use CUDA/OpenCL/Metal APIs
113            Some(cpu_data.clone())
114        } else {
115            // In real implementation, we would query GPU memory size and transfer
116            // For now, return error if no CPU fallback is available
117            return Err(TimeSeriesError::NotImplemented(
118                "GPU memory reconstruction not implemented without CPU fallback".to_string(),
119            ));
120        };
121
122        Ok(Self {
123            cpu_data: transferred_data,
124            gpu_handle: None, // Release GPU handle after transfer
125            config: self.config.clone(),
126            on_gpu: false,
127        })
128    }
129
130    fn is_on_gpu(&self) -> bool {
131        self.on_gpu
132    }
133
134    fn gpu_memory_usage(&self) -> usize {
135        if self.on_gpu {
136            self.len() * std::mem::size_of::<F>()
137        } else {
138            0
139        }
140    }
141}
scirs2_series/gpu_acceleration/array.rs

scirs2_series/gpu_acceleration/
array.rs
