scirs2_linalg/simd_ops/neural/
cache.rs

1//! Cache management and prediction module for neural memory optimization.
2
3use super::types::*;
4use crate::error::{LinalgError, LinalgResult};
5use scirs2_core::ndarray::{Array1, Array2, Array3};
6use scirs2_core::numeric::{Float, NumAssign, Zero};
7use std::collections::{HashMap, VecDeque};
8use std::fmt::Debug;
9
10/// Neural cache prediction model using deep learning
11#[derive(Debug)]
12#[allow(dead_code)]
13pub struct NeuralCachePredictionModel<T> {
14    /// Convolutional layers for pattern recognition
15    conv_layers: Vec<ConvolutionalLayer<T>>,
16    /// LSTM layers for temporal modeling
17    lstm_layers: Vec<LstmLayer<T>>,
18    /// Dense layers for prediction
19    dense_layers: Vec<DenseLayer<T>>,
20    /// Prediction accuracy history
21    accuracy_history: VecDeque<f64>,
22    /// Training data buffer
23    training_buffer: VecDeque<CacheAccessPattern<T>>,
24    /// Model parameters
25    model_params: NeuralModelParameters,
26}
27
28/// Convolutional layer for spatial pattern recognition
29#[derive(Debug)]
30pub struct ConvolutionalLayer<T> {
31    /// Kernel weights
32    pub kernels: Array3<T>,
33    /// Bias terms
34    pub biases: Array1<T>,
35    /// Stride
36    pub stride: (usize, usize),
37    /// Padding
38    pub padding: (usize, usize),
39    /// Activation function
40    pub activation: ActivationFunction,
41}
42
43/// LSTM layer for temporal sequence modeling
44#[derive(Debug)]
45pub struct LstmLayer<T> {
46    /// Input gate weights
47    pub input_weights: Array2<T>,
48    /// Forget gate weights
49    pub forget_weights: Array2<T>,
50    /// Output gate weights
51    pub output_weights: Array2<T>,
52    /// Cell state weights
53    pub cell_weights: Array2<T>,
54    /// Hidden state
55    pub hidden_state: Array1<T>,
56    /// Cell state
57    pub cell_state: Array1<T>,
58}
59
60/// Dense (fully connected) layer
61#[derive(Debug)]
62pub struct DenseLayer<T> {
63    /// Weight matrix
64    pub weights: Array2<T>,
65    /// Bias vector
66    pub biases: Array1<T>,
67    /// Activation function
68    pub activation: ActivationFunction,
69    /// Dropout rate
70    pub dropout_rate: f64,
71}
72
73/// Cache access pattern for training
74#[derive(Debug, Clone)]
75pub struct CacheAccessPattern<T> {
76    /// Memory addresses accessed
77    pub addresses: Vec<usize>,
78    /// Access order
79    pub access_order: Vec<usize>,
80    /// Data types
81    pub data_types: Vec<DataType>,
82    /// Access sizes
83    pub accesssizes: Vec<usize>,
84    /// Temporal spacing
85    pub temporal_spacing: Vec<f64>,
86    /// Spatial locality score
87    pub spatial_locality: f64,
88    /// Temporal locality score
89    pub temporal_locality: f64,
90    /// Cache hit/miss pattern
91    pub hit_miss_pattern: Vec<bool>,
92    /// Context information
93    pub context: AccessContext<T>,
94}
95
96/// Neural model parameters
97#[derive(Debug, Clone)]
98pub struct NeuralModelParameters {
99    /// Learning rate
100    pub learning_rate: f64,
101    /// Batch size
102    pub batchsize: usize,
103    /// Number of epochs
104    pub epochs: usize,
105    /// Regularization strength
106    pub regularization: f64,
107    /// Dropout rate
108    pub dropout_rate: f64,
109    /// Early stopping patience
110    pub early_stopping_patience: usize,
111    /// Validation split
112    pub validation_split: f64,
113    /// Optimizer type
114    pub optimizer: OptimizerType,
115}
116
117/// Optimizer types for neural network training
118#[derive(Debug, Clone, PartialEq)]
119pub enum OptimizerType {
120    SGD,
121    Adam,
122    RMSprop,
123    AdaGrad,
124    AdaDelta,
125    Nadam,
126    Custom(String),
127}
128
129/// Cache performance prediction result
130#[derive(Debug, Clone)]
131pub struct CachePerformancePrediction {
132    /// Predicted cache hit rate
133    pub hit_rate: f64,
134    /// Predicted average latency
135    pub average_latency: f64,
136    /// Confidence in prediction
137    pub confidence: f64,
138    /// Bottleneck analysis
139    pub bottlenecks: Vec<PerformanceBottleneck>,
140}
141
142/// Performance bottleneck identification
143#[derive(Debug, Clone)]
144pub struct PerformanceBottleneck {
145    /// Bottleneck type
146    pub bottleneck_type: BottleneckType,
147    /// Severity score
148    pub severity: f64,
149    /// Mitigation suggestions
150    pub mitigation: String,
151}
152
153/// Types of performance bottlenecks
154#[derive(Debug, Clone, PartialEq)]
155pub enum BottleneckType {
156    CacheMiss,
157    MemoryBandwidth,
158    TLBMiss,
159    NumaTraffic,
160    FalseSharing,
161    Contention,
162}
163
164/// Training metrics
165#[derive(Debug, Clone)]
166pub struct TrainingMetrics {
167    /// Loss value
168    pub loss: f64,
169    /// Accuracy
170    pub accuracy: f64,
171    /// Validation loss
172    pub validation_loss: f64,
173    /// Validation accuracy
174    pub validation_accuracy: f64,
175    /// Epoch number
176    pub epoch: usize,
177    /// Training time
178    pub training_time: std::time::Duration,
179}
180
181/// Bandwidth measurement
182#[derive(Debug, Clone)]
183pub struct BandwidthMeasurement {
184    /// Timestamp
185    pub timestamp: std::time::Instant,
186    /// Read bandwidth (GB/s)
187    pub read_bandwidth: f64,
188    /// Write bandwidth (GB/s)
189    pub write_bandwidth: f64,
190    /// Total bandwidth utilization
191    pub total_utilization: f64,
192    /// Memory pressure
193    pub memory_pressure: f64,
194    /// Queue depth
195    pub queue_depth: usize,
196}
197
198/// Bandwidth monitor for memory subsystem
199#[derive(Debug)]
200#[allow(dead_code)]
201pub struct BandwidthMonitor {
202    /// Current bandwidth utilization
203    current_utilization: f64,
204    /// Bandwidth history
205    bandwidth_history: VecDeque<BandwidthMeasurement>,
206    /// Saturation detector
207    saturation_detector: SaturationDetector,
208    /// Prediction model
209    bandwidth_predictor: BandwidthPredictor,
210}
211
212/// Saturation detector for memory bandwidth
213#[derive(Debug)]
214#[allow(dead_code)]
215pub struct SaturationDetector {
216    /// Saturation threshold
217    saturation_threshold: f64,
218    /// Detection algorithm
219    detection_algorithm: SaturationDetectionAlgorithm,
220    /// Current saturation level
221    current_saturation: f64,
222    /// Saturation history
223    saturation_history: VecDeque<f64>,
224}
225
226/// Algorithms for detecting bandwidth saturation
227#[derive(Debug, Clone, PartialEq)]
228pub enum SaturationDetectionAlgorithm {
229    ThresholdBased,
230    TrendAnalysis,
231    StatisticalAnomalyDetection,
232    MachineLearning,
233    HybridApproach,
234}
235
236/// Bandwidth predictor
237#[derive(Debug)]
238#[allow(dead_code)]
239pub struct BandwidthPredictor {
240    /// Prediction model
241    model: BandwidthPredictionModel,
242    /// Historical accuracy
243    accuracy: f64,
244    /// Prediction horizon
245    prediction_horizon: std::time::Duration,
246}
247
248/// Bandwidth prediction models
249#[derive(Debug)]
250pub enum BandwidthPredictionModel {
251    ARIMA,
252    LSTM,
253    Prophet,
254    LinearRegression,
255    Ensemble(Vec<Box<BandwidthPredictionModel>>),
256}
257
258/// Bandwidth saturation prediction
259#[derive(Debug, Clone)]
260pub struct BandwidthSaturationPrediction {
261    /// Predicted saturation level
262    pub saturation_level: f64,
263    /// Time to saturation
264    pub time_to_saturation: Option<std::time::Duration>,
265    /// Confidence in prediction
266    pub confidence: f64,
267    /// Recommended actions
268    pub recommendations: Vec<String>,
269}
270
271// Implementations
272impl<T> NeuralCachePredictionModel<T>
273where
274    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
275{
276    pub fn new() -> LinalgResult<Self> {
277        Ok(Self {
278            conv_layers: Vec::new(),
279            lstm_layers: Vec::new(),
280            dense_layers: Vec::new(),
281            accuracy_history: VecDeque::new(),
282            training_buffer: VecDeque::new(),
283            model_params: NeuralModelParameters::default(),
284        })
285    }
286
287    pub fn predict_performance(
288        &self,
289        _pattern: &CacheAccessPattern<T>,
290    ) -> LinalgResult<CachePerformancePrediction> {
291        // Simplified prediction
292        Ok(CachePerformancePrediction {
293            hit_rate: 0.85,
294            average_latency: 2.5,
295            confidence: 0.9,
296            bottlenecks: Vec::new(),
297        })
298    }
299}
300
301impl Default for NeuralModelParameters {
302    fn default() -> Self {
303        Self {
304            learning_rate: 0.001,
305            batchsize: 32,
306            epochs: 100,
307            regularization: 0.01,
308            dropout_rate: 0.1,
309            early_stopping_patience: 10,
310            validation_split: 0.2,
311            optimizer: OptimizerType::Adam,
312        }
313    }
314}
315
316impl<T> DenseLayer<T>
317where
318    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
319{
320    pub fn new() -> LinalgResult<Self> {
321        Ok(Self {
322            weights: Array2::zeros((1, 1)),
323            biases: Array1::zeros(1),
324            activation: ActivationFunction::ReLU,
325            dropout_rate: 0.0,
326        })
327    }
328}
329
330impl BandwidthMonitor {
331    pub fn new() -> LinalgResult<Self> {
332        Ok(Self {
333            current_utilization: 0.0,
334            bandwidth_history: VecDeque::new(),
335            saturation_detector: SaturationDetector::new(),
336            bandwidth_predictor: BandwidthPredictor::new(),
337        })
338    }
339
340    pub fn predict_saturation(&self) -> LinalgResult<BandwidthSaturationPrediction> {
341        Ok(BandwidthSaturationPrediction {
342            saturation_level: 0.3,
343            time_to_saturation: Some(std::time::Duration::from_secs(60)),
344            confidence: 0.85,
345            recommendations: vec!["Reduce memory traffic".to_string()],
346        })
347    }
348}
349
350impl Default for SaturationDetector {
351    fn default() -> Self {
352        Self::new()
353    }
354}
355
356impl SaturationDetector {
357    pub fn new() -> Self {
358        Self {
359            saturation_threshold: 0.8,
360            detection_algorithm: SaturationDetectionAlgorithm::ThresholdBased,
361            current_saturation: 0.0,
362            saturation_history: VecDeque::new(),
363        }
364    }
365}
366
367impl Default for BandwidthPredictor {
368    fn default() -> Self {
369        Self::new()
370    }
371}
372
373impl BandwidthPredictor {
374    pub fn new() -> Self {
375        Self {
376            model: BandwidthPredictionModel::LinearRegression,
377            accuracy: 0.8,
378            prediction_horizon: std::time::Duration::from_secs(30),
379        }
380    }
381}
382
383impl<T> CacheAccessPattern<T>
384where
385    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
386{
387    pub fn from_workload(workload: &WorkloadCharacteristics) -> Self {
388        Self {
389            addresses: Vec::new(),
390            access_order: Vec::new(),
391            data_types: Vec::new(),
392            accesssizes: Vec::new(),
393            temporal_spacing: Vec::new(),
394            spatial_locality: 0.5,
395            temporal_locality: 0.5,
396            hit_miss_pattern: Vec::new(),
397            context: AccessContext::default(),
398        }
399    }
400}
scirs2_linalg/simd_ops/neural/cache.rs

scirs2_linalg/simd_ops/neural/
cache.rs
