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().unwrap();
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().unwrap();
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().unwrap();
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(t, y, h / F::from(2.0).unwrap(), &f)?;
347        let y2 = self.advanced_rk4_step(
348            t + h / F::from(2.0).unwrap(),
349            &y_half1.view(),
350            h / F::from(2.0).unwrap(),
351            &f,
352        )?;
353
354        // Calculate error estimate using GPU-accelerated norm computation
355        let error = self.advanced_gpu_error_estimate(&y1.view(), &y2.view(), rtol, atol)?;
356
357        // Determine step acceptance and new step size
358        let safety_factor = F::from(0.9).unwrap();
359        let error_tolerance = F::one();
360
361        if error <= error_tolerance {
362            // Accept step with error-based step size update
363            let factor = safety_factor * (error_tolerance / error).powf(F::from(0.2).unwrap());
364            let new_h = h * factor.min(F::from(2.0).unwrap()).max(F::from(0.5).unwrap());
365            Ok((y2, new_h, true))
366        } else {
367            // Reject step and reduce step size
368            let factor = safety_factor * (error_tolerance / error).powf(F::from(0.25).unwrap());
369            let new_h = h * factor.max(F::from(0.1).unwrap());
370            Ok((y.to_owned(), new_h, false))
371        }
372    }
373
374    /// Launch optimized RK4 stage 1 kernel
375    fn launch_rk4_stage1_kernel(
376        &self,
377        y: &MemoryBlock<F>,
378        k1: &MemoryBlock<F>,
379        t: F,
380        h: F,
381        config: &KernelConfiguration,
382    ) -> IntegrateResult<()> {
383        let context = self.context.lock().unwrap();
384
385        // Launch kernel with optimal thread configuration
386        context
387            .launch_kernel(
388                "rk4_stage1",
389                config.grid_size,
390                config.block_size,
391                &[
392                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
393                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
394                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
395                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
396                    DynamicKernelArg::Usize(y.size),
397                ],
398            )
399            .map_err(|e| {
400                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
401            })?;
402
403        Ok(())
404    }
405
406    /// Launch optimized RK4 stage 2 kernel
407    fn launch_rk4_stage2_kernel(
408        &self,
409        y: &MemoryBlock<F>,
410        k1: &MemoryBlock<F>,
411        k2: &MemoryBlock<F>,
412        t: F,
413        h: F,
414        config: &KernelConfiguration,
415    ) -> IntegrateResult<()> {
416        let context = self.context.lock().unwrap();
417
418        context
419            .launch_kernel(
420                "rk4_stage2",
421                config.grid_size,
422                config.block_size,
423                &[
424                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
425                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
426                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
427                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
428                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
429                    DynamicKernelArg::Usize(y.size),
430                ],
431            )
432            .map_err(|e| {
433                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
434            })?;
435
436        Ok(())
437    }
438
439    /// Launch optimized RK4 stage 3 kernel
440    fn launch_rk4_stage3_kernel(
441        &self,
442        y: &MemoryBlock<F>,
443        k2: &MemoryBlock<F>,
444        k3: &MemoryBlock<F>,
445        t: F,
446        h: F,
447        config: &KernelConfiguration,
448    ) -> IntegrateResult<()> {
449        let context = self.context.lock().unwrap();
450
451        context
452            .launch_kernel(
453                "rk4_stage3",
454                config.grid_size,
455                config.block_size,
456                &[
457                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
458                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
459                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
460                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
461                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
462                    DynamicKernelArg::Usize(y.size),
463                ],
464            )
465            .map_err(|e| {
466                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
467            })?;
468
469        Ok(())
470    }
471
472    /// Launch optimized RK4 stage 4 kernel
473    fn launch_rk4_stage4_kernel(
474        &self,
475        y: &MemoryBlock<F>,
476        k3: &MemoryBlock<F>,
477        k4: &MemoryBlock<F>,
478        t: F,
479        h: F,
480        config: &KernelConfiguration,
481    ) -> IntegrateResult<()> {
482        let context = self.context.lock().unwrap();
483
484        context
485            .launch_kernel(
486                "rk4_stage4",
487                config.grid_size,
488                config.block_size,
489                &[
490                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
491                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
492                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
493                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
494                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
495                    DynamicKernelArg::Usize(y.size),
496                ],
497            )
498            .map_err(|e| {
499                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
500            })?;
501
502        Ok(())
503    }
504
505    /// Launch optimized RK4 combination kernel
506    fn launch_rk4_combine_kernel(
507        &self,
508        y: &MemoryBlock<F>,
509        k1: &MemoryBlock<F>,
510        k2: &MemoryBlock<F>,
511        k3: &MemoryBlock<F>,
512        k4: &MemoryBlock<F>,
513        result: &MemoryBlock<F>,
514        h: F,
515        config: &KernelConfiguration,
516    ) -> IntegrateResult<()> {
517        let context = self.context.lock().unwrap();
518
519        context
520            .launch_kernel(
521                "rk4_combine",
522                config.grid_size,
523                config.block_size,
524                &[
525                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
526                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
527                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
528                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
529                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
530                    DynamicKernelArg::Buffer(result.gpu_ptr.as_ptr()),
531                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
532                    DynamicKernelArg::Usize(y.size),
533                ],
534            )
535            .map_err(|e| {
536                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
537            })?;
538
539        Ok(())
540    }
541
542    /// Transfer data to GPU with optimized memory access patterns
543    fn transfer_to_gpu_optimized(
544        &self,
545        gpu_block: &MemoryBlock<F>,
546        data: &ArrayView1<F>,
547    ) -> IntegrateResult<()> {
548        let context = self.context.lock().unwrap();
549
550        // Use optimal transfer strategy based on data size
551        if data.len() > 10000 {
552            // Use asynchronous transfer for large data
553            context
554                .transfer_async_host_to_device(&gpu_block.gpu_ptr, data.as_slice().unwrap())
555                .map_err(|e| {
556                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
557                })?;
558        } else {
559            // Use synchronous transfer for small data
560            context
561                .transfer_host_to_device(&gpu_block.gpu_ptr, data.as_slice().unwrap())
562                .map_err(|e| {
563                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
564                })?;
565        }
566
567        Ok(())
568    }
569
570    /// Transfer data from GPU with optimized memory access patterns
571    fn transfer_from_gpu_optimized(
572        &self,
573        gpu_block: &MemoryBlock<F>,
574    ) -> IntegrateResult<Array1<F>> {
575        let context = self.context.lock().unwrap();
576
577        let mut result = vec![F::zero(); gpu_block.size];
578
579        // Use optimal transfer strategy based on data size
580        if gpu_block.size > 10000 {
581            // Use asynchronous transfer for large data
582            context
583                .transfer_async_device_to_host(&gpu_block.gpu_ptr, &mut result)
584                .map_err(|e| {
585                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
586                })?;
587        } else {
588            // Use synchronous transfer for small data
589            context
590                .transfer_device_to_host(&gpu_block.gpu_ptr, &mut result)
591                .map_err(|e| {
592                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
593                })?;
594        }
595
596        Ok(Array1::from_vec(result))
597    }
598
599    /// Get or optimize kernel configuration for maximum performance
600    fn get_or_optimize_kernel_config(
601        &self,
602        cache: &mut HashMap<String, KernelPerformanceData>,
603        kernel_name: &str,
604        problem_size: usize,
605    ) -> IntegrateResult<KernelConfiguration> {
606        // Check if we have cached optimal configuration
607        if let Some(perf_data) = cache.get(kernel_name) {
608            if perf_data.last_optimized.elapsed() < Duration::from_secs(300) {
609                // Use cached configuration if recent
610                return Ok(KernelConfiguration {
611                    block_size: perf_data.optimal_block_size,
612                    grid_size: Self::calculate_grid_size(
613                        problem_size,
614                        perf_data.optimal_block_size.0,
615                    ),
616                });
617            }
618        }
619
620        // Perform auto-tuning to find optimal configuration
621        self.auto_tune_kernel(kernel_name, problem_size)
622    }
623
624    /// Auto-tune kernel for optimal performance
625    fn auto_tune_kernel(
626        &self,
627        kernel_name: &str,
628        problem_size: usize,
629    ) -> IntegrateResult<KernelConfiguration> {
630        let mut best_config = KernelConfiguration {
631            block_size: (256, 1, 1),
632            grid_size: Self::calculate_grid_size(problem_size, 256),
633        };
634        let mut best_time = Duration::from_secs(u64::MAX);
635
636        // Test different block sizes
637        let block_sizes = [32, 64, 128, 256, 512, 1024];
638
639        for &block_size in &block_sizes {
640            if block_size > problem_size {
641                continue;
642            }
643
644            let config = KernelConfiguration {
645                block_size: (block_size, 1, 1),
646                grid_size: Self::calculate_grid_size(problem_size, block_size),
647            };
648
649            // Benchmark this configuration
650            let execution_time =
651                self.benchmark_kernel_config(kernel_name, &config, problem_size)?;
652
653            if execution_time < best_time {
654                best_time = execution_time;
655                best_config = config;
656            }
657        }
658
659        Ok(best_config)
660    }
661
662    /// Benchmark a specific kernel configuration
663    fn benchmark_kernel_config(
664        &self,
665        _kernel_name: &str,
666        _config: &KernelConfiguration,
667        problem_size: usize,
668    ) -> IntegrateResult<Duration> {
669        // Simplified benchmark - in real implementation would launch actual kernels
670        Ok(Duration::from_micros(100))
671    }
672
673    /// Calculate optimal grid size for given problem size and block size
674    fn calculate_grid_size(problem_size: usize, blocksize: usize) -> (usize, usize, usize) {
675        let grid_size = problem_size.div_ceil(blocksize);
676        (grid_size, 1, 1)
677    }
678
679    /// Compute GPU-accelerated error estimate
680    fn advanced_gpu_error_estimate(
681        &self,
682        y1: &ArrayView1<F>,
683        y2: &ArrayView1<F>,
684        rtol: F,
685        atol: F,
686    ) -> IntegrateResult<F> {
687        // Allocate GPU memory for error computation
688        let mut memory_pool = self.memory_pool.lock().unwrap();
689        let y1_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
690        let y2_gpu = memory_pool.allocate_temporary_vector(y2.len())?;
691        let error_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
692        drop(memory_pool);
693
694        // Transfer data to GPU
695        self.transfer_to_gpu_optimized(&y1_gpu, y1)?;
696        self.transfer_to_gpu_optimized(&y2_gpu, y2)?;
697
698        // Launch error computation kernel
699        let context = self.context.lock().unwrap();
700        context
701            .launch_kernel(
702                "error_estimate",
703                Self::calculate_grid_size(y1.len(), 256),
704                (256, 1, 1),
705                &[
706                    DynamicKernelArg::Buffer(y1_gpu.gpu_ptr.as_ptr()),
707                    DynamicKernelArg::Buffer(y2_gpu.gpu_ptr.as_ptr()),
708                    DynamicKernelArg::Buffer(error_gpu.gpu_ptr.as_ptr()),
709                    DynamicKernelArg::F64(rtol.to_f64().unwrap_or(0.0)),
710                    DynamicKernelArg::F64(atol.to_f64().unwrap_or(0.0)),
711                    DynamicKernelArg::Usize(y1.len()),
712                ],
713            )
714            .map_err(|e| {
715                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
716            })?;
717        drop(context);
718
719        // Get result back from GPU
720        let error_vec = self.transfer_from_gpu_optimized(&error_gpu)?;
721        let error = error_vec.iter().fold(F::zero(), |acc, &x| acc.max(x));
722
723        // Cleanup GPU memory
724        let mut memory_pool = self.memory_pool.lock().unwrap();
725        memory_pool.deallocate(y1_gpu.id)?;
726        memory_pool.deallocate(y2_gpu.id)?;
727        memory_pool.deallocate(error_gpu.id)?;
728
729        Ok(error)
730    }
731
732    /// Update kernel performance data for adaptive optimization
733    fn update_kernel_performance(
734        &self,
735        kernel_name: &str,
736        execution_time: Duration,
737        config: &KernelConfiguration,
738    ) -> IntegrateResult<()> {
739        let mut cache = self.kernel_cache.lock().unwrap();
740
741        let perf_data =
742            cache
743                .entry(kernel_name.to_string())
744                .or_insert_with(|| KernelPerformanceData {
745                    avg_execution_time: execution_time,
746                    execution_count: 0,
747                    optimal_block_size: config.block_size,
748                    memory_bandwidth_usage: 0.0,
749                    compute_utilization: 0.0,
750                    last_optimized: Instant::now(),
751                });
752
753        // Update moving average of execution _time
754        perf_data.execution_count += 1;
755        let alpha = 0.1; // Exponential moving average factor
756        let old_avg = perf_data.avg_execution_time.as_nanos() as f64;
757        let new_time = execution_time.as_nanos() as f64;
758        let new_avg = old_avg * (1.0 - alpha) + new_time * alpha;
759        perf_data.avg_execution_time = Duration::from_nanos(new_avg as u64);
760
761        Ok(())
762    }
763}
764
765/// Kernel launch configuration
766#[derive(Debug, Clone)]
767pub struct KernelConfiguration {
768    /// Thread block dimensions (x, y, z)
769    pub block_size: (usize, usize, usize),
770    /// Grid dimensions (x, y, z)
771    pub grid_size: (usize, usize, usize),
772}
773
774impl<F: IntegrateFloat + GpuDataType> AdvancedGPUMemoryPool<F> {
775    /// Create a new advanced-optimized GPU memory pool
776    pub fn new() -> IntegrateResult<Self> {
777        Ok(AdvancedGPUMemoryPool {
778            available_blocks: Vec::new(),
779            allocated_blocks: HashMap::new(),
780            total_memory: 0,
781            used_memory: 0,
782            fragmentation_ratio: 0.0,
783            defrag_threshold: 0.3,
784            block_counter: 0,
785        })
786    }
787
788    /// Create a new memory pool with CPU fallback mode
789    pub fn new_cpu_fallback() -> IntegrateResult<Self> {
790        Ok(AdvancedGPUMemoryPool {
791            available_blocks: Vec::new(),
792            allocated_blocks: HashMap::new(),
793            total_memory: 1024 * 1024 * 1024, // 1GB virtual CPU memory
794            used_memory: 0,
795            fragmentation_ratio: 0.0,
796            defrag_threshold: 0.3,
797            block_counter: 0,
798        })
799    }
800
801    /// Allocate memory for solution vectors with optimization
802    pub fn allocate_solution_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
803        self.allocate_block(size, MemoryBlockType::Solution)
804    }
805
806    /// Allocate memory for derivative vectors with optimization
807    pub fn allocate_derivative_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
808        self.allocate_block(size, MemoryBlockType::Derivative)
809    }
810
811    /// Allocate memory for temporary vectors
812    pub fn allocate_temporary_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
813        self.allocate_block(size, MemoryBlockType::Temporary)
814    }
815
816    /// Generic block allocation with type-aware optimization
817    fn allocate_block(
818        &mut self,
819        size: usize,
820        block_type: MemoryBlockType,
821    ) -> IntegrateResult<MemoryBlock<F>> {
822        self.block_counter += 1;
823
824        // Try to find a suitable existing block
825        if let Some(index) = self.find_suitable_block(size) {
826            let mut block = self.available_blocks.remove(index);
827            block.id = self.block_counter;
828            block.block_type = block_type.clone();
829            block.allocated_time = Instant::now();
830            block.usage_count += 1;
831
832            // Track allocation metadata
833            self.allocated_blocks
834                .insert(block.id, (block.size, block_type, block.allocated_time));
835            self.used_memory += block.size * std::mem::size_of::<F>();
836
837            return Ok(block);
838        }
839
840        // Allocate new block if none suitable found
841        let gpu_ptr = gpu::GpuPtr::allocate(size).map_err(|e| {
842            IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}"))
843        })?;
844        let allocated_time = Instant::now();
845        let block = MemoryBlock {
846            id: self.block_counter,
847            gpu_ptr,
848            size,
849            allocated_time,
850            usage_count: 1,
851            block_type: block_type.clone(),
852        };
853
854        // Track allocation metadata
855        self.allocated_blocks
856            .insert(block.id, (size, block_type, allocated_time));
857        self.used_memory += size * std::mem::size_of::<F>();
858
859        Ok(block)
860    }
861
862    /// Find a suitable available block for reuse
863    fn find_suitable_block(&self, _requiredsize: usize) -> Option<usize> {
864        for (index, block) in self.available_blocks.iter().enumerate() {
865            // Block should be within 25% of required _size for efficient reuse
866            if block.size >= _requiredsize && block.size <= _requiredsize * 5 / 4 {
867                return Some(index);
868            }
869        }
870        None
871    }
872
873    /// Deallocate a memory block back to the pool
874    pub fn deallocate(&mut self, blockid: usize) -> IntegrateResult<()> {
875        if let Some((size__, mem_type, timestamp)) = self.allocated_blocks.remove(&blockid) {
876            self.used_memory -= size__ * std::mem::size_of::<F>();
877
878            // Note: In a real implementation, we would properly return the block to available_blocks
879            // For now, we just track the deallocation without maintaining the available blocks
880            // since we can't clone GpuPtr easily
881
882            // Trigger defragmentation if needed
883            self.update_fragmentation_metrics();
884            if self.fragmentation_ratio > self.defrag_threshold {
885                self.defragment()?;
886            }
887
888            Ok(())
889        } else {
890            Err(IntegrateError::ValueError(format!(
891                "Block {blockid} not found"
892            )))
893        }
894    }
895
896    /// Update fragmentation metrics
897    fn update_fragmentation_metrics(&mut self) {
898        if self.total_memory == 0 {
899            self.fragmentation_ratio = 0.0;
900            return;
901        }
902
903        let total_available = self.available_blocks.iter().map(|b| b.size).sum::<usize>();
904        let largest_available = self
905            .available_blocks
906            .iter()
907            .map(|b| b.size)
908            .max()
909            .unwrap_or(0);
910
911        if total_available == 0 {
912            self.fragmentation_ratio = 0.0;
913        } else {
914            self.fragmentation_ratio = 1.0 - (largest_available as f64 / total_available as f64);
915        }
916    }
917
918    /// Defragment GPU memory pool
919    fn defragment(&mut self) -> IntegrateResult<()> {
920        // Sort available blocks by size for better allocation patterns
921        self.available_blocks.sort_by_key(|block| block.size);
922
923        // Merge adjacent blocks if possible (simplified implementation)
924        let mut merged_blocks = Vec::new();
925        for block in self.available_blocks.drain(..) {
926            merged_blocks.push(block);
927        }
928
929        self.available_blocks = merged_blocks;
930        self.update_fragmentation_metrics();
931
932        Ok(())
933    }
934}
935
936impl MultiGpuConfiguration {
937    /// Detect and configure multi-GPU setup
938    pub fn detect_and_configure(&self) -> IntegrateResult<Self> {
939        let devices = self.detect_gpu_devices()?;
940        let load_balancing = LoadBalancingStrategy::Adaptive;
941        let communication_channels = Vec::new(); // Would be initialized with actual GPU channels
942        let workload_ratios = Self::calculate_initial_ratios(&devices);
943
944        Ok(MultiGpuConfiguration {
945            devices,
946            load_balancing,
947            communication_channels,
948            workload_ratios,
949        })
950    }
951
952    /// Create a CPU fallback configuration
953    pub fn cpu_fallback_config(&self) -> IntegrateResult<Self> {
954        let devices = vec![GpuDeviceInfo {
955            device_id: 0,
956            name: "CPU Fallback Mode".to_string(),
957            total_memory: 8 * 1024 * 1024 * 1024, // 8GB system RAM
958            compute_capability: (1, 0),           // Minimal capability
959            multiprocessor_count: num_cpus::get(),
960            max_threads_per_block: 1,
961            current_load: 0.0,
962        }];
963        let load_balancing = LoadBalancingStrategy::RoundRobin;
964        let communication_channels = Vec::new();
965        let workload_ratios = vec![1.0];
966
967        Ok(MultiGpuConfiguration {
968            devices,
969            load_balancing,
970            communication_channels,
971            workload_ratios,
972        })
973    }
974
975    /// Detect available GPU devices
976    fn detect_gpu_devices(&self) -> IntegrateResult<Vec<GpuDeviceInfo>> {
977        // Simplified detection - real implementation would query GPU drivers
978        Ok(vec![GpuDeviceInfo {
979            device_id: 0,
980            name: "NVIDIA RTX 4090".to_string(),
981            total_memory: 24 * 1024 * 1024 * 1024, // 24GB
982            compute_capability: (8, 9),
983            multiprocessor_count: 128,
984            max_threads_per_block: 1024,
985            current_load: 0.0,
986        }])
987    }
988
989    /// Calculate initial workload distribution ratios
990    fn calculate_initial_ratios(devices: &[GpuDeviceInfo]) -> Vec<f64> {
991        let total_compute_power: usize = devices
992            .iter()
993            .map(|d| d.multiprocessor_count * d.max_threads_per_block)
994            .sum();
995
996        devices
997            .iter()
998            .map(|d| {
999                let device_power = d.multiprocessor_count * d.max_threads_per_block;
1000                device_power as f64 / total_compute_power as f64
1001            })
1002            .collect()
1003    }
1004}
1005
1006impl RealTimeGpuMonitor {
1007    /// Create a new real-time GPU performance monitor
1008    pub fn new() -> Self {
1009        RealTimeGpuMonitor {
1010            metrics_history: Vec::new(),
1011            monitoring_interval: Duration::from_millis(100),
1012            thresholds: PerformanceThresholds {
1013                max_gpu_utilization: 95.0,
1014                max_memory_utilization: 90.0,
1015                max_temperature: 85.0,
1016                min_efficiency: 80.0,
1017            },
1018            adaptive_optimization: true,
1019        }
1020    }
1021
1022    /// Start real-time monitoring (would spawn background thread in real implementation)
1023    pub fn start_monitoring(&self) -> IntegrateResult<()> {
1024        // Simplified - real implementation would start background monitoring
1025        Ok(())
1026    }
1027
1028    /// Get current performance metrics
1029    pub fn get_current_metrics(&self) -> Option<&GpuPerformanceMetrics> {
1030        self.metrics_history.last()
1031    }
1032
1033    /// Check if performance optimization is needed
1034    pub fn needs_optimization(&self) -> bool {
1035        if let Some(metrics) = self.get_current_metrics() {
1036            metrics.gpu_utilization > self.thresholds.max_gpu_utilization
1037                || metrics.memory_utilization > self.thresholds.max_memory_utilization
1038                || metrics.temperature > self.thresholds.max_temperature
1039        } else {
1040            false
1041        }
1042    }
1043}
1044
1045impl Default for PerformanceThresholds {
1046    fn default() -> Self {
1047        PerformanceThresholds {
1048            max_gpu_utilization: 95.0,
1049            max_memory_utilization: 90.0,
1050            max_temperature: 85.0,
1051            min_efficiency: 80.0,
1052        }
1053    }
1054}
1055
1056#[cfg(test)]
1057mod tests {
1058    use super::*;
1059
1060    #[test]
1061    fn test_gpu_memory_pool_allocation() {
1062        let mut pool = AdvancedGPUMemoryPool::<f64>::new().unwrap();
1063
1064        // Test basic allocation
1065        let block1 = pool.allocate_solution_vector(1000);
1066        assert!(block1.is_ok());
1067
1068        let block2 = pool.allocate_derivative_vector(500);
1069        assert!(block2.is_ok());
1070
1071        // Test deallocation
1072        if let Ok(block) = block1 {
1073            assert!(pool.deallocate(block.id).is_ok());
1074        }
1075    }
1076
1077    #[test]
1078    fn test_multi_gpu_configuration() {
1079        let detector = MultiGpuConfiguration::default();
1080        let config = detector.detect_and_configure();
1081        assert!(config.is_ok());
1082
1083        if let Ok(cfg) = config {
1084            assert!(!cfg.devices.is_empty());
1085            assert_eq!(cfg.workload_ratios.len(), cfg.devices.len());
1086        }
1087    }
1088
1089    #[test]
1090    fn test_performance_monitor() {
1091        let monitor = RealTimeGpuMonitor::new();
1092        assert!(monitor.start_monitoring().is_ok());
1093        assert!(!monitor.needs_optimization()); // No metrics yet
1094    }
1095}
scirs2_integrate/gpu_advanced_acceleration.rs

scirs2_integrate/
gpu_advanced_acceleration.rs
