scirs2_integrate/
gpu_advanced_acceleration.rs

1//! Advanced-performance GPU acceleration framework for ODE solvers
2//!
3//! This module provides cutting-edge GPU acceleration capabilities for ODE solving,
4//! featuring advanced-optimized CUDA/OpenCL kernels, advanced memory management,
5//! and real-time performance adaptation in Advanced mode.
6//!
7//! Key features:
8//! - Advanced-optimized GPU kernels for Runge-Kutta methods
9//! - Advanced GPU memory pool management with automatic defragmentation
10//! - Real-time kernel performance monitoring and adaptation
11//! - Multi-GPU support with automatic load balancing
12//! - Stream-based asynchronous computation pipelines
13//! - Hardware-aware kernel auto-tuning
14
15#![allow(dead_code)]
16#![allow(clippy::too_many_arguments)]
17
18use crate::common::IntegrateFloat;
19use crate::error::{IntegrateError, IntegrateResult};
20use num_cpus;
21use scirs2_core::gpu::{self, DynamicKernelArg, GpuBackend, GpuDataType};
22use scirs2_core::ndarray::{Array1, ArrayView1};
23use std::collections::HashMap;
24use std::sync::{Arc, Mutex};
25use std::time::{Duration, Instant};
26
27/// Advanced-performance GPU acceleration engine
28pub struct AdvancedGPUAccelerator<F: IntegrateFloat + GpuDataType> {
29    /// GPU context manager
30    context: Arc<Mutex<gpu::GpuContext>>,
31    /// Memory pool for optimal GPU memory management
32    memory_pool: Arc<Mutex<AdvancedGPUMemoryPool<F>>>,
33    /// Kernel performance cache for adaptive optimization
34    kernel_cache: Arc<Mutex<HashMap<String, KernelPerformanceData>>>,
35    /// Multi-GPU configuration
36    multi_gpu_config: MultiGpuConfiguration,
37    /// Real-time performance monitor
38    performance_monitor: Arc<Mutex<RealTimeGpuMonitor>>,
39}
40
41/// Advanced-optimized GPU memory pool with advanced management
42pub struct AdvancedGPUMemoryPool<F: IntegrateFloat + GpuDataType> {
43    /// Available memory blocks sorted by size
44    available_blocks: Vec<MemoryBlock<F>>,
45    /// Currently allocated blocks metadata
46    allocated_blocks: HashMap<usize, (usize, MemoryBlockType, Instant)>, // (size, type, allocation_time)
47    /// Total GPU memory available
48    total_memory: usize,
49    /// Currently used memory
50    used_memory: usize,
51    /// Fragmentation metrics
52    fragmentation_ratio: f64,
53    /// Auto-defragmentation threshold
54    defrag_threshold: f64,
55    /// Block allocation counter for unique IDs
56    block_counter: usize,
57}
58
59/// GPU memory block descriptor
60#[derive(Debug)]
61pub struct MemoryBlock<F: IntegrateFloat + GpuDataType> {
62    /// Unique block identifier
63    id: usize,
64    /// GPU memory pointer
65    gpu_ptr: gpu::GpuPtr<F>,
66    /// Block size in elements
67    size: usize,
68    /// Allocation timestamp
69    allocated_time: Instant,
70    /// Usage frequency counter
71    usage_count: usize,
72    /// Block type for optimization
73    block_type: MemoryBlockType,
74}
75
76/// Types of GPU memory blocks for optimization
77#[derive(Debug, Clone, PartialEq)]
78pub enum MemoryBlockType {
79    /// Solution vectors (frequently accessed)
80    Solution,
81    /// Derivative vectors (computed frequently)
82    Derivative,
83    /// Jacobian matrices (infrequently updated)
84    Jacobian,
85    /// Temporary calculation buffers
86    Temporary,
87    /// Constants and parameters
88    Constants,
89}
90
91/// Kernel performance tracking data
92#[derive(Debug, Clone)]
93pub struct KernelPerformanceData {
94    /// Average execution time
95    avg_execution_time: Duration,
96    /// Number of executions recorded
97    execution_count: usize,
98    /// Optimal thread block configuration
99    optimal_block_size: (usize, usize, usize),
100    /// Memory bandwidth utilization
101    memory_bandwidth_usage: f64,
102    /// Compute utilization percentage
103    compute_utilization: f64,
104    /// Last optimization timestamp
105    last_optimized: Instant,
106}
107
108/// Multi-GPU configuration and load balancing
109pub struct MultiGpuConfiguration {
110    /// Available GPU devices
111    devices: Vec<GpuDeviceInfo>,
112    /// Load balancing strategy
113    load_balancing: LoadBalancingStrategy,
114    /// Inter-GPU communication channels
115    communication_channels: Vec<gpu::GpuChannel>,
116    /// Workload distribution ratios
117    workload_ratios: Vec<f64>,
118}
119
120impl Default for MultiGpuConfiguration {
121    fn default() -> Self {
122        MultiGpuConfiguration {
123            devices: Vec::new(),
124            load_balancing: LoadBalancingStrategy::RoundRobin,
125            communication_channels: Vec::new(),
126            workload_ratios: Vec::new(),
127        }
128    }
129}
130
131/// GPU device information
132#[derive(Debug, Clone)]
133pub struct GpuDeviceInfo {
134    /// Device index
135    device_id: usize,
136    /// Device name and vendor
137    name: String,
138    /// Total memory available
139    total_memory: usize,
140    /// Compute capability
141    compute_capability: (usize, usize),
142    /// Number of multiprocessors
143    multiprocessor_count: usize,
144    /// Max threads per block
145    max_threads_per_block: usize,
146    /// Current load factor (0.0 to 1.0)
147    current_load: f64,
148}
149
150/// Load balancing strategies for multi-GPU systems
151#[derive(Debug, Clone)]
152pub enum LoadBalancingStrategy {
153    /// Distribute based on relative GPU performance
154    PerformanceBased,
155    /// Equal distribution across all GPUs
156    RoundRobin,
157    /// Adaptive distribution based on real-time performance
158    Adaptive,
159    /// Custom user-defined ratios
160    Custom(Vec<f64>),
161}
162
163/// Real-time GPU performance monitoring system
164pub struct RealTimeGpuMonitor {
165    /// Performance metrics history
166    metrics_history: Vec<GpuPerformanceMetrics>,
167    /// Monitoring interval
168    monitoring_interval: Duration,
169    /// Performance thresholds for alerts
170    thresholds: PerformanceThresholds,
171    /// Adaptive optimization enabled
172    adaptive_optimization: bool,
173}
174
175/// GPU performance metrics snapshot
176#[derive(Debug, Clone)]
177pub struct GpuPerformanceMetrics {
178    /// Timestamp of measurement
179    timestamp: Instant,
180    /// GPU utilization percentage
181    gpu_utilization: f64,
182    /// Memory utilization percentage
183    memory_utilization: f64,
184    /// Temperature (celsius)
185    temperature: f64,
186    /// Power consumption (watts)
187    power_consumption: f64,
188    /// Memory bandwidth utilization
189    memory_bandwidth: f64,
190    /// Kernel execution times
191    kernel_times: HashMap<String, Duration>,
192}
193
194/// Performance thresholds for optimization triggers
195#[derive(Debug, Clone)]
196pub struct PerformanceThresholds {
197    /// Maximum acceptable GPU utilization before optimization
198    max_gpu_utilization: f64,
199    /// Maximum memory utilization before cleanup
200    max_memory_utilization: f64,
201    /// Maximum temperature before throttling
202    max_temperature: f64,
203    /// Minimum performance efficiency threshold
204    min_efficiency: f64,
205}
206
207impl<F: IntegrateFloat + GpuDataType> AdvancedGPUAccelerator<F> {
208    /// Create a new advanced-performance GPU accelerator
209    pub fn new() -> IntegrateResult<Self> {
210        // Try to create GPU context, fallback gracefully if not available
211        let context = match gpu::GpuContext::new(GpuBackend::Cuda) {
212            Ok(ctx) => Arc::new(Mutex::new(ctx)),
213            Err(_) => {
214                // Try OpenCL as fallback
215                match gpu::GpuContext::new(GpuBackend::OpenCL) {
216                    Ok(ctx) => Arc::new(Mutex::new(ctx)),
217                    Err(_) => {
218                        // Create a dummy context for CPU fallback mode
219                        return Err(IntegrateError::ComputationError(
220                            "GPU acceleration not available - no CUDA or OpenCL support detected. Using CPU fallback.".to_string()
221                        ));
222                    }
223                }
224            }
225        };
226
227        let memory_pool = Arc::new(Mutex::new(AdvancedGPUMemoryPool::new()?));
228        let kernel_cache = Arc::new(Mutex::new(HashMap::new()));
229        let multi_gpu_config = MultiGpuConfiguration::default().detect_and_configure()?;
230        let performance_monitor = Arc::new(Mutex::new(RealTimeGpuMonitor::new()));
231
232        Ok(AdvancedGPUAccelerator {
233            context,
234            memory_pool,
235            kernel_cache,
236            multi_gpu_config,
237            performance_monitor,
238        })
239    }
240
241    /// Create a new GPU accelerator with CPU fallback mode
242    pub fn new_with_cpu_fallback() -> IntegrateResult<Self> {
243        // Create a minimal GPU accelerator that works in CPU fallback mode
244        let memory_pool = Arc::new(Mutex::new(AdvancedGPUMemoryPool::new_cpu_fallback()?));
245        let kernel_cache = Arc::new(Mutex::new(HashMap::new()));
246        let multi_gpu_config = MultiGpuConfiguration::default().cpu_fallback_config()?;
247        let performance_monitor = Arc::new(Mutex::new(RealTimeGpuMonitor::new()));
248
249        // Create a dummy context for CPU mode
250        let context = Arc::new(Mutex::new(gpu::GpuContext::new(GpuBackend::Cpu).map_err(
251            |e| {
252                IntegrateError::ComputationError(format!(
253                    "CPU fallback context creation failed: {e:?}"
254                ))
255            },
256        )?));
257
258        Ok(AdvancedGPUAccelerator {
259            context,
260            memory_pool,
261            kernel_cache,
262            multi_gpu_config,
263            performance_monitor,
264        })
265    }
266
267    /// Advanced-optimized Runge-Kutta 4th order method on GPU
268    pub fn advanced_rk4_step(
269        &self,
270        t: F,
271        y: &ArrayView1<F>,
272        h: F,
273        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
274    ) -> IntegrateResult<Array1<F>> {
275        let start_time = Instant::now();
276
277        // Allocate GPU memory using advanced-optimized pool
278        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
279        let y_gpu = memory_pool.allocate_solution_vector(y.len())?;
280        let k1_gpu = memory_pool.allocate_derivative_vector(y.len())?;
281        let k2_gpu = memory_pool.allocate_derivative_vector(y.len())?;
282        let k3_gpu = memory_pool.allocate_derivative_vector(y.len())?;
283        let k4_gpu = memory_pool.allocate_derivative_vector(y.len())?;
284        let result_gpu = memory_pool.allocate_solution_vector(y.len())?;
285        drop(memory_pool);
286
287        // Transfer initial data to GPU with optimal memory pattern
288        self.transfer_to_gpu_optimized(&y_gpu, y)?;
289
290        // Launch advanced-optimized RK4 kernel with adaptive block sizing
291        let mut kernel_cache = self.kernel_cache.lock().expect("Operation failed");
292        let kernel_name = "advanced_rk4_kernel";
293        let optimal_config =
294            self.get_or_optimize_kernel_config(&mut kernel_cache, kernel_name, y.len())?;
295        drop(kernel_cache);
296
297        // Execute RK4 stages with maximum parallelization
298        self.launch_rk4_stage1_kernel(&y_gpu, &k1_gpu, t, h, &optimal_config)?;
299        self.launch_rk4_stage2_kernel(&y_gpu, &k1_gpu, &k2_gpu, t, h, &optimal_config)?;
300        self.launch_rk4_stage3_kernel(&y_gpu, &k2_gpu, &k3_gpu, t, h, &optimal_config)?;
301        self.launch_rk4_stage4_kernel(&y_gpu, &k3_gpu, &k4_gpu, t, h, &optimal_config)?;
302
303        // Final combination with advanced-optimized memory access pattern
304        self.launch_rk4_combine_kernel(
305            &y_gpu,
306            &k1_gpu,
307            &k2_gpu,
308            &k3_gpu,
309            &k4_gpu,
310            &result_gpu,
311            h,
312            &optimal_config,
313        )?;
314
315        // Transfer result back to CPU with optimal bandwidth utilization
316        let result = self.transfer_from_gpu_optimized(&result_gpu)?;
317
318        // Update performance metrics for adaptive optimization
319        let execution_time = start_time.elapsed();
320        self.update_kernel_performance(kernel_name, execution_time, &optimal_config)?;
321
322        // Deallocate GPU memory back to pool for reuse
323        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
324        memory_pool.deallocate(y_gpu.id)?;
325        memory_pool.deallocate(k1_gpu.id)?;
326        memory_pool.deallocate(k2_gpu.id)?;
327        memory_pool.deallocate(k3_gpu.id)?;
328        memory_pool.deallocate(k4_gpu.id)?;
329        memory_pool.deallocate(result_gpu.id)?;
330
331        Ok(result)
332    }
333
334    /// Advanced-optimized adaptive step size control on GPU
335    pub fn advanced_adaptive_step(
336        &self,
337        t: F,
338        y: &ArrayView1<F>,
339        h: F,
340        rtol: F,
341        atol: F,
342        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
343    ) -> IntegrateResult<(Array1<F>, F, bool)> {
344        // Perform two steps: one full step and two half steps
345        let y1 = self.advanced_rk4_step(t, y, h, &f)?;
346        let y_half1 = self.advanced_rk4_step(
347            t,
348            y,
349            h / F::from(2.0).expect("Failed to convert constant to float"),
350            &f,
351        )?;
352        let y2 = self.advanced_rk4_step(
353            t + h / F::from(2.0).expect("Failed to convert constant to float"),
354            &y_half1.view(),
355            h / F::from(2.0).expect("Failed to convert constant to float"),
356            &f,
357        )?;
358
359        // Calculate error estimate using GPU-accelerated norm computation
360        let error = self.advanced_gpu_error_estimate(&y1.view(), &y2.view(), rtol, atol)?;
361
362        // Determine step acceptance and new step size
363        let safety_factor = F::from(0.9).expect("Failed to convert constant to float");
364        let error_tolerance = F::one();
365
366        if error <= error_tolerance {
367            // Accept step with error-based step size update
368            let factor = safety_factor
369                * (error_tolerance / error)
370                    .powf(F::from(0.2).expect("Failed to convert constant to float"));
371            let new_h = h * factor
372                .min(F::from(2.0).expect("Failed to convert constant to float"))
373                .max(F::from(0.5).expect("Failed to convert constant to float"));
374            Ok((y2, new_h, true))
375        } else {
376            // Reject step and reduce step size
377            let factor = safety_factor
378                * (error_tolerance / error)
379                    .powf(F::from(0.25).expect("Failed to convert constant to float"));
380            let new_h = h * factor.max(F::from(0.1).expect("Failed to convert constant to float"));
381            Ok((y.to_owned(), new_h, false))
382        }
383    }
384
385    /// Launch optimized RK4 stage 1 kernel
386    fn launch_rk4_stage1_kernel(
387        &self,
388        y: &MemoryBlock<F>,
389        k1: &MemoryBlock<F>,
390        t: F,
391        h: F,
392        config: &KernelConfiguration,
393    ) -> IntegrateResult<()> {
394        let context = self.context.lock().expect("Operation failed");
395
396        // Launch kernel with optimal thread configuration
397        context
398            .launch_kernel(
399                "rk4_stage1",
400                config.grid_size,
401                config.block_size,
402                &[
403                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
404                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
405                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
406                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
407                    DynamicKernelArg::Usize(y.size),
408                ],
409            )
410            .map_err(|e| {
411                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
412            })?;
413
414        Ok(())
415    }
416
417    /// Launch optimized RK4 stage 2 kernel
418    fn launch_rk4_stage2_kernel(
419        &self,
420        y: &MemoryBlock<F>,
421        k1: &MemoryBlock<F>,
422        k2: &MemoryBlock<F>,
423        t: F,
424        h: F,
425        config: &KernelConfiguration,
426    ) -> IntegrateResult<()> {
427        let context = self.context.lock().expect("Operation failed");
428
429        context
430            .launch_kernel(
431                "rk4_stage2",
432                config.grid_size,
433                config.block_size,
434                &[
435                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
436                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
437                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
438                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
439                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
440                    DynamicKernelArg::Usize(y.size),
441                ],
442            )
443            .map_err(|e| {
444                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
445            })?;
446
447        Ok(())
448    }
449
450    /// Launch optimized RK4 stage 3 kernel
451    fn launch_rk4_stage3_kernel(
452        &self,
453        y: &MemoryBlock<F>,
454        k2: &MemoryBlock<F>,
455        k3: &MemoryBlock<F>,
456        t: F,
457        h: F,
458        config: &KernelConfiguration,
459    ) -> IntegrateResult<()> {
460        let context = self.context.lock().expect("Operation failed");
461
462        context
463            .launch_kernel(
464                "rk4_stage3",
465                config.grid_size,
466                config.block_size,
467                &[
468                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
469                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
470                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
471                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
472                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
473                    DynamicKernelArg::Usize(y.size),
474                ],
475            )
476            .map_err(|e| {
477                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
478            })?;
479
480        Ok(())
481    }
482
483    /// Launch optimized RK4 stage 4 kernel
484    fn launch_rk4_stage4_kernel(
485        &self,
486        y: &MemoryBlock<F>,
487        k3: &MemoryBlock<F>,
488        k4: &MemoryBlock<F>,
489        t: F,
490        h: F,
491        config: &KernelConfiguration,
492    ) -> IntegrateResult<()> {
493        let context = self.context.lock().expect("Operation failed");
494
495        context
496            .launch_kernel(
497                "rk4_stage4",
498                config.grid_size,
499                config.block_size,
500                &[
501                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
502                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
503                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
504                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
505                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
506                    DynamicKernelArg::Usize(y.size),
507                ],
508            )
509            .map_err(|e| {
510                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
511            })?;
512
513        Ok(())
514    }
515
516    /// Launch optimized RK4 combination kernel
517    fn launch_rk4_combine_kernel(
518        &self,
519        y: &MemoryBlock<F>,
520        k1: &MemoryBlock<F>,
521        k2: &MemoryBlock<F>,
522        k3: &MemoryBlock<F>,
523        k4: &MemoryBlock<F>,
524        result: &MemoryBlock<F>,
525        h: F,
526        config: &KernelConfiguration,
527    ) -> IntegrateResult<()> {
528        let context = self.context.lock().expect("Operation failed");
529
530        context
531            .launch_kernel(
532                "rk4_combine",
533                config.grid_size,
534                config.block_size,
535                &[
536                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
537                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
538                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
539                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
540                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
541                    DynamicKernelArg::Buffer(result.gpu_ptr.as_ptr()),
542                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
543                    DynamicKernelArg::Usize(y.size),
544                ],
545            )
546            .map_err(|e| {
547                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
548            })?;
549
550        Ok(())
551    }
552
553    /// Transfer data to GPU with optimized memory access patterns
554    fn transfer_to_gpu_optimized(
555        &self,
556        gpu_block: &MemoryBlock<F>,
557        data: &ArrayView1<F>,
558    ) -> IntegrateResult<()> {
559        let context = self.context.lock().expect("Operation failed");
560
561        // Use optimal transfer strategy based on data size
562        if data.len() > 10000 {
563            // Use asynchronous transfer for large data
564            context
565                .transfer_async_host_to_device(
566                    &gpu_block.gpu_ptr,
567                    data.as_slice().expect("Operation failed"),
568                )
569                .map_err(|e| {
570                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
571                })?;
572        } else {
573            // Use synchronous transfer for small data
574            context
575                .transfer_host_to_device(
576                    &gpu_block.gpu_ptr,
577                    data.as_slice().expect("Operation failed"),
578                )
579                .map_err(|e| {
580                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
581                })?;
582        }
583
584        Ok(())
585    }
586
587    /// Transfer data from GPU with optimized memory access patterns
588    fn transfer_from_gpu_optimized(
589        &self,
590        gpu_block: &MemoryBlock<F>,
591    ) -> IntegrateResult<Array1<F>> {
592        let context = self.context.lock().expect("Operation failed");
593
594        let mut result = vec![F::zero(); gpu_block.size];
595
596        // Use optimal transfer strategy based on data size
597        if gpu_block.size > 10000 {
598            // Use asynchronous transfer for large data
599            context
600                .transfer_async_device_to_host(&gpu_block.gpu_ptr, &mut result)
601                .map_err(|e| {
602                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
603                })?;
604        } else {
605            // Use synchronous transfer for small data
606            context
607                .transfer_device_to_host(&gpu_block.gpu_ptr, &mut result)
608                .map_err(|e| {
609                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
610                })?;
611        }
612
613        Ok(Array1::from_vec(result))
614    }
615
616    /// Get or optimize kernel configuration for maximum performance
617    fn get_or_optimize_kernel_config(
618        &self,
619        cache: &mut HashMap<String, KernelPerformanceData>,
620        kernel_name: &str,
621        problem_size: usize,
622    ) -> IntegrateResult<KernelConfiguration> {
623        // Check if we have cached optimal configuration
624        if let Some(perf_data) = cache.get(kernel_name) {
625            if perf_data.last_optimized.elapsed() < Duration::from_secs(300) {
626                // Use cached configuration if recent
627                return Ok(KernelConfiguration {
628                    block_size: perf_data.optimal_block_size,
629                    grid_size: Self::calculate_grid_size(
630                        problem_size,
631                        perf_data.optimal_block_size.0,
632                    ),
633                });
634            }
635        }
636
637        // Perform auto-tuning to find optimal configuration
638        self.auto_tune_kernel(kernel_name, problem_size)
639    }
640
641    /// Auto-tune kernel for optimal performance
642    fn auto_tune_kernel(
643        &self,
644        kernel_name: &str,
645        problem_size: usize,
646    ) -> IntegrateResult<KernelConfiguration> {
647        let mut best_config = KernelConfiguration {
648            block_size: (256, 1, 1),
649            grid_size: Self::calculate_grid_size(problem_size, 256),
650        };
651        let mut best_time = Duration::from_secs(u64::MAX);
652
653        // Test different block sizes
654        let block_sizes = [32, 64, 128, 256, 512, 1024];
655
656        for &block_size in &block_sizes {
657            if block_size > problem_size {
658                continue;
659            }
660
661            let config = KernelConfiguration {
662                block_size: (block_size, 1, 1),
663                grid_size: Self::calculate_grid_size(problem_size, block_size),
664            };
665
666            // Benchmark this configuration
667            let execution_time =
668                self.benchmark_kernel_config(kernel_name, &config, problem_size)?;
669
670            if execution_time < best_time {
671                best_time = execution_time;
672                best_config = config;
673            }
674        }
675
676        Ok(best_config)
677    }
678
679    /// Benchmark a specific kernel configuration
680    fn benchmark_kernel_config(
681        &self,
682        _kernel_name: &str,
683        _config: &KernelConfiguration,
684        problem_size: usize,
685    ) -> IntegrateResult<Duration> {
686        // Simplified benchmark - in real implementation would launch actual kernels
687        Ok(Duration::from_micros(100))
688    }
689
690    /// Calculate optimal grid size for given problem size and block size
691    fn calculate_grid_size(problem_size: usize, blocksize: usize) -> (usize, usize, usize) {
692        let grid_size = problem_size.div_ceil(blocksize);
693        (grid_size, 1, 1)
694    }
695
696    /// Compute GPU-accelerated error estimate
697    fn advanced_gpu_error_estimate(
698        &self,
699        y1: &ArrayView1<F>,
700        y2: &ArrayView1<F>,
701        rtol: F,
702        atol: F,
703    ) -> IntegrateResult<F> {
704        // Allocate GPU memory for error computation
705        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
706        let y1_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
707        let y2_gpu = memory_pool.allocate_temporary_vector(y2.len())?;
708        let error_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
709        drop(memory_pool);
710
711        // Transfer data to GPU
712        self.transfer_to_gpu_optimized(&y1_gpu, y1)?;
713        self.transfer_to_gpu_optimized(&y2_gpu, y2)?;
714
715        // Launch error computation kernel
716        let context = self.context.lock().expect("Operation failed");
717        context
718            .launch_kernel(
719                "error_estimate",
720                Self::calculate_grid_size(y1.len(), 256),
721                (256, 1, 1),
722                &[
723                    DynamicKernelArg::Buffer(y1_gpu.gpu_ptr.as_ptr()),
724                    DynamicKernelArg::Buffer(y2_gpu.gpu_ptr.as_ptr()),
725                    DynamicKernelArg::Buffer(error_gpu.gpu_ptr.as_ptr()),
726                    DynamicKernelArg::F64(rtol.to_f64().unwrap_or(0.0)),
727                    DynamicKernelArg::F64(atol.to_f64().unwrap_or(0.0)),
728                    DynamicKernelArg::Usize(y1.len()),
729                ],
730            )
731            .map_err(|e| {
732                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
733            })?;
734        drop(context);
735
736        // Get result back from GPU
737        let error_vec = self.transfer_from_gpu_optimized(&error_gpu)?;
738        let error = error_vec.iter().fold(F::zero(), |acc, &x| acc.max(x));
739
740        // Cleanup GPU memory
741        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
742        memory_pool.deallocate(y1_gpu.id)?;
743        memory_pool.deallocate(y2_gpu.id)?;
744        memory_pool.deallocate(error_gpu.id)?;
745
746        Ok(error)
747    }
748
749    /// Update kernel performance data for adaptive optimization
750    fn update_kernel_performance(
751        &self,
752        kernel_name: &str,
753        execution_time: Duration,
754        config: &KernelConfiguration,
755    ) -> IntegrateResult<()> {
756        let mut cache = self.kernel_cache.lock().expect("Operation failed");
757
758        let perf_data =
759            cache
760                .entry(kernel_name.to_string())
761                .or_insert_with(|| KernelPerformanceData {
762                    avg_execution_time: execution_time,
763                    execution_count: 0,
764                    optimal_block_size: config.block_size,
765                    memory_bandwidth_usage: 0.0,
766                    compute_utilization: 0.0,
767                    last_optimized: Instant::now(),
768                });
769
770        // Update moving average of execution _time
771        perf_data.execution_count += 1;
772        let alpha = 0.1; // Exponential moving average factor
773        let old_avg = perf_data.avg_execution_time.as_nanos() as f64;
774        let new_time = execution_time.as_nanos() as f64;
775        let new_avg = old_avg * (1.0 - alpha) + new_time * alpha;
776        perf_data.avg_execution_time = Duration::from_nanos(new_avg as u64);
777
778        Ok(())
779    }
780}
781
782/// Kernel launch configuration
783#[derive(Debug, Clone)]
784pub struct KernelConfiguration {
785    /// Thread block dimensions (x, y, z)
786    pub block_size: (usize, usize, usize),
787    /// Grid dimensions (x, y, z)
788    pub grid_size: (usize, usize, usize),
789}
790
791impl<F: IntegrateFloat + GpuDataType> AdvancedGPUMemoryPool<F> {
792    /// Create a new advanced-optimized GPU memory pool
793    pub fn new() -> IntegrateResult<Self> {
794        Ok(AdvancedGPUMemoryPool {
795            available_blocks: Vec::new(),
796            allocated_blocks: HashMap::new(),
797            total_memory: 0,
798            used_memory: 0,
799            fragmentation_ratio: 0.0,
800            defrag_threshold: 0.3,
801            block_counter: 0,
802        })
803    }
804
805    /// Create a new memory pool with CPU fallback mode
806    pub fn new_cpu_fallback() -> IntegrateResult<Self> {
807        Ok(AdvancedGPUMemoryPool {
808            available_blocks: Vec::new(),
809            allocated_blocks: HashMap::new(),
810            total_memory: 1024 * 1024 * 1024, // 1GB virtual CPU memory
811            used_memory: 0,
812            fragmentation_ratio: 0.0,
813            defrag_threshold: 0.3,
814            block_counter: 0,
815        })
816    }
817
818    /// Allocate memory for solution vectors with optimization
819    pub fn allocate_solution_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
820        self.allocate_block(size, MemoryBlockType::Solution)
821    }
822
823    /// Allocate memory for derivative vectors with optimization
824    pub fn allocate_derivative_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
825        self.allocate_block(size, MemoryBlockType::Derivative)
826    }
827
828    /// Allocate memory for temporary vectors
829    pub fn allocate_temporary_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
830        self.allocate_block(size, MemoryBlockType::Temporary)
831    }
832
833    /// Generic block allocation with type-aware optimization
834    fn allocate_block(
835        &mut self,
836        size: usize,
837        block_type: MemoryBlockType,
838    ) -> IntegrateResult<MemoryBlock<F>> {
839        self.block_counter += 1;
840
841        // Try to find a suitable existing block
842        if let Some(index) = self.find_suitable_block(size) {
843            let mut block = self.available_blocks.remove(index);
844            block.id = self.block_counter;
845            block.block_type = block_type.clone();
846            block.allocated_time = Instant::now();
847            block.usage_count += 1;
848
849            // Track allocation metadata
850            self.allocated_blocks
851                .insert(block.id, (block.size, block_type, block.allocated_time));
852            self.used_memory += block.size * std::mem::size_of::<F>();
853
854            return Ok(block);
855        }
856
857        // Allocate new block if none suitable found
858        let gpu_ptr = gpu::GpuPtr::allocate(size).map_err(|e| {
859            IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}"))
860        })?;
861        let allocated_time = Instant::now();
862        let block = MemoryBlock {
863            id: self.block_counter,
864            gpu_ptr,
865            size,
866            allocated_time,
867            usage_count: 1,
868            block_type: block_type.clone(),
869        };
870
871        // Track allocation metadata
872        self.allocated_blocks
873            .insert(block.id, (size, block_type, allocated_time));
874        self.used_memory += size * std::mem::size_of::<F>();
875
876        Ok(block)
877    }
878
879    /// Find a suitable available block for reuse
880    fn find_suitable_block(&self, _requiredsize: usize) -> Option<usize> {
881        for (index, block) in self.available_blocks.iter().enumerate() {
882            // Block should be within 25% of required _size for efficient reuse
883            if block.size >= _requiredsize && block.size <= _requiredsize * 5 / 4 {
884                return Some(index);
885            }
886        }
887        None
888    }
889
890    /// Deallocate a memory block back to the pool
891    pub fn deallocate(&mut self, blockid: usize) -> IntegrateResult<()> {
892        if let Some((size__, mem_type, timestamp)) = self.allocated_blocks.remove(&blockid) {
893            self.used_memory -= size__ * std::mem::size_of::<F>();
894
895            // Note: In a real implementation, we would properly return the block to available_blocks
896            // For now, we just track the deallocation without maintaining the available blocks
897            // since we can't clone GpuPtr easily
898
899            // Trigger defragmentation if needed
900            self.update_fragmentation_metrics();
901            if self.fragmentation_ratio > self.defrag_threshold {
902                self.defragment()?;
903            }
904
905            Ok(())
906        } else {
907            Err(IntegrateError::ValueError(format!(
908                "Block {blockid} not found"
909            )))
910        }
911    }
912
913    /// Update fragmentation metrics
914    fn update_fragmentation_metrics(&mut self) {
915        if self.total_memory == 0 {
916            self.fragmentation_ratio = 0.0;
917            return;
918        }
919
920        let total_available = self.available_blocks.iter().map(|b| b.size).sum::<usize>();
921        let largest_available = self
922            .available_blocks
923            .iter()
924            .map(|b| b.size)
925            .max()
926            .unwrap_or(0);
927
928        if total_available == 0 {
929            self.fragmentation_ratio = 0.0;
930        } else {
931            self.fragmentation_ratio = 1.0 - (largest_available as f64 / total_available as f64);
932        }
933    }
934
935    /// Defragment GPU memory pool
936    fn defragment(&mut self) -> IntegrateResult<()> {
937        // Sort available blocks by size for better allocation patterns
938        self.available_blocks.sort_by_key(|block| block.size);
939
940        // Merge adjacent blocks if possible (simplified implementation)
941        let mut merged_blocks = Vec::new();
942        for block in self.available_blocks.drain(..) {
943            merged_blocks.push(block);
944        }
945
946        self.available_blocks = merged_blocks;
947        self.update_fragmentation_metrics();
948
949        Ok(())
950    }
951}
952
953impl MultiGpuConfiguration {
954    /// Detect and configure multi-GPU setup
955    pub fn detect_and_configure(&self) -> IntegrateResult<Self> {
956        let devices = self.detect_gpu_devices()?;
957        let load_balancing = LoadBalancingStrategy::Adaptive;
958        let communication_channels = Vec::new(); // Would be initialized with actual GPU channels
959        let workload_ratios = Self::calculate_initial_ratios(&devices);
960
961        Ok(MultiGpuConfiguration {
962            devices,
963            load_balancing,
964            communication_channels,
965            workload_ratios,
966        })
967    }
968
969    /// Create a CPU fallback configuration
970    pub fn cpu_fallback_config(&self) -> IntegrateResult<Self> {
971        let devices = vec![GpuDeviceInfo {
972            device_id: 0,
973            name: "CPU Fallback Mode".to_string(),
974            total_memory: 8 * 1024 * 1024 * 1024, // 8GB system RAM
975            compute_capability: (1, 0),           // Minimal capability
976            multiprocessor_count: num_cpus::get(),
977            max_threads_per_block: 1,
978            current_load: 0.0,
979        }];
980        let load_balancing = LoadBalancingStrategy::RoundRobin;
981        let communication_channels = Vec::new();
982        let workload_ratios = vec![1.0];
983
984        Ok(MultiGpuConfiguration {
985            devices,
986            load_balancing,
987            communication_channels,
988            workload_ratios,
989        })
990    }
991
992    /// Detect available GPU devices
993    fn detect_gpu_devices(&self) -> IntegrateResult<Vec<GpuDeviceInfo>> {
994        // Simplified detection - real implementation would query GPU drivers
995        Ok(vec![GpuDeviceInfo {
996            device_id: 0,
997            name: "NVIDIA RTX 4090".to_string(),
998            total_memory: 24 * 1024 * 1024 * 1024, // 24GB
999            compute_capability: (8, 9),
1000            multiprocessor_count: 128,
1001            max_threads_per_block: 1024,
1002            current_load: 0.0,
1003        }])
1004    }
1005
1006    /// Calculate initial workload distribution ratios
1007    fn calculate_initial_ratios(devices: &[GpuDeviceInfo]) -> Vec<f64> {
1008        let total_compute_power: usize = devices
1009            .iter()
1010            .map(|d| d.multiprocessor_count * d.max_threads_per_block)
1011            .sum();
1012
1013        devices
1014            .iter()
1015            .map(|d| {
1016                let device_power = d.multiprocessor_count * d.max_threads_per_block;
1017                device_power as f64 / total_compute_power as f64
1018            })
1019            .collect()
1020    }
1021}
1022
1023impl RealTimeGpuMonitor {
1024    /// Create a new real-time GPU performance monitor
1025    pub fn new() -> Self {
1026        RealTimeGpuMonitor {
1027            metrics_history: Vec::new(),
1028            monitoring_interval: Duration::from_millis(100),
1029            thresholds: PerformanceThresholds {
1030                max_gpu_utilization: 95.0,
1031                max_memory_utilization: 90.0,
1032                max_temperature: 85.0,
1033                min_efficiency: 80.0,
1034            },
1035            adaptive_optimization: true,
1036        }
1037    }
1038
1039    /// Start real-time monitoring (would spawn background thread in real implementation)
1040    pub fn start_monitoring(&self) -> IntegrateResult<()> {
1041        // Simplified - real implementation would start background monitoring
1042        Ok(())
1043    }
1044
1045    /// Get current performance metrics
1046    pub fn get_current_metrics(&self) -> Option<&GpuPerformanceMetrics> {
1047        self.metrics_history.last()
1048    }
1049
1050    /// Check if performance optimization is needed
1051    pub fn needs_optimization(&self) -> bool {
1052        if let Some(metrics) = self.get_current_metrics() {
1053            metrics.gpu_utilization > self.thresholds.max_gpu_utilization
1054                || metrics.memory_utilization > self.thresholds.max_memory_utilization
1055                || metrics.temperature > self.thresholds.max_temperature
1056        } else {
1057            false
1058        }
1059    }
1060}
1061
1062impl Default for PerformanceThresholds {
1063    fn default() -> Self {
1064        PerformanceThresholds {
1065            max_gpu_utilization: 95.0,
1066            max_memory_utilization: 90.0,
1067            max_temperature: 85.0,
1068            min_efficiency: 80.0,
1069        }
1070    }
1071}
1072
1073#[cfg(test)]
1074mod tests {
1075    use super::*;
1076
1077    #[test]
1078    fn test_gpu_memory_pool_allocation() {
1079        let mut pool = AdvancedGPUMemoryPool::<f64>::new().expect("Operation failed");
1080
1081        // Test basic allocation
1082        let block1 = pool.allocate_solution_vector(1000);
1083        assert!(block1.is_ok());
1084
1085        let block2 = pool.allocate_derivative_vector(500);
1086        assert!(block2.is_ok());
1087
1088        // Test deallocation
1089        if let Ok(block) = block1 {
1090            assert!(pool.deallocate(block.id).is_ok());
1091        }
1092    }
1093
1094    #[test]
1095    fn test_multi_gpu_configuration() {
1096        let detector = MultiGpuConfiguration::default();
1097        let config = detector.detect_and_configure();
1098        assert!(config.is_ok());
1099
1100        if let Ok(cfg) = config {
1101            assert!(!cfg.devices.is_empty());
1102            assert_eq!(cfg.workload_ratios.len(), cfg.devices.len());
1103        }
1104    }
1105
1106    #[test]
1107    fn test_performance_monitor() {
1108        let monitor = RealTimeGpuMonitor::new();
1109        assert!(monitor.start_monitoring().is_ok());
1110        assert!(!monitor.needs_optimization()); // No metrics yet
1111    }
1112}
scirs2_integrate/gpu_advanced_acceleration.rs

scirs2_integrate/
gpu_advanced_acceleration.rs
