Implement enhanced parallelism capabilities with modern C++ features

Co-authored-by: WenyinWei <[email protected]>

Author: Copilot

include/diffeq.hpp

@@ -36,6 +36,13 @@
 #include <signal/signal_processor.hpp>   // Generic signal processing
 #include <interfaces/integration_interface.hpp>  // Unified interface for all domains
 
+// Enhanced parallelism capabilities with modern C++ features
+#include <execution/parallelism_facade.hpp>      // Unified parallelism interface (Facade pattern)
+#include <execution/parallelism_facade_impl.hpp> // Implementation details
+#include <execution/parallel_builder.hpp>        // Fluent interface for configuration (Builder pattern)
+#include <execution/modern_executor.hpp>         // Modern C++ executor support with coroutines
+#include <execution/hardware_support.hpp>        // Hardware-specific execution (CUDA, OpenCL, FPGA, MPI)
+
 /**
  * @file diffeq.hpp
  * @brief Modern C++ ODE Integration Library with Real-time Signal Processing

include/examples/parallelism_usage.hpp

@@ -0,0 +1,341 @@
+#pragma once
+
+#include <diffeq.hpp>
+#include <vector>
+#include <iostream>
+#include <chrono>
+#include <random>
+
+namespace diffeq::examples::parallelism {
+
+/**
+ * @brief Robotics Control Systems Example
+ * 
+ * Demonstrates real-time integration with feedback signals from sensors
+ * with low latency and deterministic performance requirements.
+ */
+namespace robotics_control {
+
+// Robot arm dynamics (simplified)
+struct RobotArmSystem {
+    double mass = 1.0;
+    double length = 0.5;
+    double damping = 0.1;
+    double gravity = 9.81;
+    
+    void operator()(double t, const std::vector<double>& state, std::vector<double>& derivative) {
+        // state = [angle, angular_velocity]
+        // Simple pendulum with control input
+        double angle = state[0];
+        double angular_vel = state[1];
+        
+        // Control input (placeholder - would come from feedback controller)
+        double control_torque = -2.0 * angle - 0.5 * angular_vel;  // PD controller
+        
+        derivative[0] = angular_vel;
+        derivative[1] = -(gravity / length) * std::sin(angle) - 
+                        (damping / (mass * length * length)) * angular_vel + 
+                        control_torque / (mass * length * length);
+    }
+};
+
+void demonstrate_realtime_control() {
+    std::cout << "\n=== Robotics Control System with Real-time Parallelism ===\n";
+    
+    // Configure for real-time robotics control
+    auto parallel_config = diffeq::execution::presets::robotics_control()
+        .realtime_priority()
+        .workers(4)                    // Dedicated cores for control
+        .batch_size(1)                 // Single step processing
+        .disable_load_balancing()      // Deterministic scheduling
+        .build();
+    
+    // Create integrator for robot dynamics
+    auto robot_system = RobotArmSystem{};
+    auto integrator = diffeq::ode::factory::make_rk4_integrator<std::vector<double>, double>(robot_system);
+    
+    // Initial state: [angle=0.1 rad, angular_velocity=0]
+    std::vector<double> state = {0.1, 0.0};
+    double dt = 0.001;  // 1ms timestep for 1kHz control
+    double simulation_time = 1.0;
+    
+    std::cout << "Running real-time robot control simulation...\n";
+    std::cout << "Target frequency: 1kHz (1ms timestep)\n";
+    
+    auto start_time = std::chrono::high_resolution_clock::now();
+    
+    // Simulate real-time control loop
+    for (double t = 0.0; t < simulation_time; t += dt) {
+        // Execute control step with real-time priority
+        auto control_future = parallel_config->async([&]() {
+            integrator->step(state, dt);
+        });
+        
+        // Simulate sensor reading (parallel)
+        auto sensor_future = parallel_config->async([&]() {
+            // Placeholder for sensor data processing
+            std::this_thread::sleep_for(std::chrono::microseconds(50));
+            return state[0];  // Return angle measurement
+        });
+        
+        // Wait for both to complete
+        control_future.wait();
+        double measured_angle = sensor_future.get();
+        
+        // Output every 100ms
+        if (static_cast<int>(t * 1000) % 100 == 0) {
+            std::cout << "t=" << t << "s, angle=" << state[0] 
+                      << " rad, measured=" << measured_angle << " rad\n";
+        }
+    }
+    
+    auto end_time = std::chrono::high_resolution_clock::now();
+    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    
+    std::cout << "Simulation completed in " << duration.count() << "ms\n";
+    std::cout << "Average loop time: " << duration.count() / (simulation_time / dt) << "ms\n";
+    std::cout << "Final robot state: angle=" << state[0] << " rad, velocity=" << state[1] << " rad/s\n";
+}
+
+} // namespace robotics_control
+
+/**
+ * @brief Stochastic Process Research Example
+ * 
+ * Demonstrates Monte Carlo simulations requiring thousands/millions of integrations
+ * with focus on throughput rather than latency.
+ */
+namespace stochastic_research {
+
+// Geometric Brownian Motion for financial modeling
+struct GeometricBrownianMotion {
+    double mu = 0.05;    // drift
+    double sigma = 0.2;  // volatility
+    
+    void operator()(double t, const std::vector<double>& state, std::vector<double>& derivative) {
+        derivative[0] = mu * state[0];  // deterministic part
+    }
+    
+    void diffusion(double t, const std::vector<double>& state, std::vector<double>& noise_coeff) {
+        noise_coeff[0] = sigma * state[0];  // stochastic part
+    }
+};
+
+void demonstrate_monte_carlo_simulation() {
+    std::cout << "\n=== Stochastic Process Research with GPU-Accelerated Monte Carlo ===\n";
+    
+    // Configure for high-throughput Monte Carlo
+    auto parallel_config = diffeq::execution::presets::monte_carlo()
+        .auto_target()                 // Let system choose best hardware
+        .workers(std::thread::hardware_concurrency() * 4)  // Oversubscribe for throughput
+        .batch_size(10000)            // Large batches for efficiency
+        .enable_load_balancing()      // Dynamic load balancing
+        .build();
+    
+    // Print available hardware
+    diffeq::execution::hardware::HardwareCapabilities::print_capabilities();
+    
+    const size_t num_simulations = 100000;
+    const double T = 1.0;           // 1 year
+    const double dt = 1.0/252;      // daily steps
+    const double S0 = 100.0;        // initial stock price
+    
+    std::cout << "\nRunning " << num_simulations << " Monte Carlo simulations...\n";
+    std::cout << "Each simulation: " << static_cast<int>(T/dt) << " steps over " << T << " years\n";
+    
+    auto start_time = std::chrono::high_resolution_clock::now();
+    
+    // Prepare simulation parameters
+    std::vector<double> final_prices(num_simulations);
+    std::vector<std::future<double>> simulation_futures;
+    
+    // Create the SDE system
+    auto gbm_system = GeometricBrownianMotion{};
+    
+    // Launch parallel simulations
+    for (size_t i = 0; i < num_simulations; ++i) {
+        simulation_futures.push_back(parallel_config->async([=]() {
+            // Create integrator for this simulation
+            auto integrator = diffeq::ode::factory::make_rk4_integrator<std::vector<double>, double>(gbm_system);
+            
+            // Random number generator for this thread
+            std::mt19937 rng(std::random_device{}() + i);
+            std::normal_distribution<double> normal(0.0, 1.0);
+            
+            std::vector<double> state = {S0};
+            double t = 0.0;
+            
+            while (t < T) {
+                // Deterministic step
+                integrator->step(state, dt);
+                
+                // Add stochastic component (simplified Euler-Maruyama)
+                double dW = normal(rng) * std::sqrt(dt);
+                state[0] += gbm_system.sigma * state[0] * dW;
+                
+                t += dt;
+            }
+            
+            return state[0];  // Return final price
+        }));
+    }
+    
+    // Collect results
+    for (size_t i = 0; i < num_simulations; ++i) {
+        final_prices[i] = simulation_futures[i].get();
+    }
+    
+    auto end_time = std::chrono::high_resolution_clock::now();
+    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    
+    // Calculate statistics
+    double mean_price = 0.0;
+    double min_price = final_prices[0];
+    double max_price = final_prices[0];
+    
+    for (double price : final_prices) {
+        mean_price += price;
+        min_price = std::min(min_price, price);
+        max_price = std::max(max_price, price);
+    }
+    mean_price /= num_simulations;
+    
+    // Calculate variance
+    double variance = 0.0;
+    for (double price : final_prices) {
+        variance += (price - mean_price) * (price - mean_price);
+    }
+    variance /= (num_simulations - 1);
+    
+    std::cout << "\nMonte Carlo Results:\n";
+    std::cout << "  Simulation time: " << duration.count() << "ms\n";
+    std::cout << "  Throughput: " << (num_simulations * 1000.0) / duration.count() << " simulations/second\n";
+    std::cout << "  Mean final price: $" << mean_price << "\n";
+    std::cout << "  Price range: $" << min_price << " - $" << max_price << "\n";
+    std::cout << "  Standard deviation: $" << std::sqrt(variance) << "\n";
+    std::cout << "  Expected price (theoretical): $" << S0 * std::exp(gbm_system.mu * T) << "\n";
+}
+
+} // namespace stochastic_research
+
+/**
+ * @brief Multi-Hardware Benchmark
+ * 
+ * Demonstrates the same computation running on different hardware targets
+ * to showcase the unified interface abstraction.
+ */
+namespace multi_hardware_demo {
+
+// Simple ODE for benchmarking: dx/dt = -k*x (exponential decay)
+struct ExponentialDecay {
+    double k = 1.0;
+    
+    void operator()(double t, const std::vector<double>& state, std::vector<double>& derivative) {
+        derivative[0] = -k * state[0];
+    }
+};
+
+void benchmark_hardware_targets() {
+    std::cout << "\n=== Multi-Hardware Parallelism Benchmark ===\n";
+    
+    const size_t num_integrations = 10000;
+    const double dt = 0.01;
+    const double end_time = 1.0;
+    const std::vector<double> initial_state = {1.0};
+    
+    std::vector<diffeq::execution::HardwareTarget> targets = {
+        diffeq::execution::HardwareTarget::CPU_Sequential,
+        diffeq::execution::HardwareTarget::CPU_ThreadPool,
+        diffeq::execution::HardwareTarget::GPU_CUDA,
+        diffeq::execution::HardwareTarget::GPU_OpenCL,
+        diffeq::execution::HardwareTarget::FPGA_HLS
+    };
+    
+    auto system = ExponentialDecay{};
+    
+    for (auto target : targets) {
+        std::string target_name;
+        switch (target) {
+            case diffeq::execution::HardwareTarget::CPU_Sequential: target_name = "CPU Sequential"; break;
+            case diffeq::execution::HardwareTarget::CPU_ThreadPool: target_name = "CPU Thread Pool"; break;
+            case diffeq::execution::HardwareTarget::GPU_CUDA: target_name = "GPU CUDA"; break;
+            case diffeq::execution::HardwareTarget::GPU_OpenCL: target_name = "GPU OpenCL"; break;
+            case diffeq::execution::HardwareTarget::FPGA_HLS: target_name = "FPGA HLS"; break;
+            default: target_name = "Unknown"; break;
+        }
+        
+        std::cout << "\nBenchmarking " << target_name << "...\n";
+        
+        // Configure parallelism for this hardware target
+        diffeq::execution::ParallelConfig config;
+        config.target = target;
+        config.max_workers = std::thread::hardware_concurrency();
+        config.batch_size = num_integrations / 10;
+        
+        auto parallel_facade = std::make_unique<diffeq::execution::ParallelismFacade>(config);
+        
+        if (!parallel_facade->is_target_available(target)) {
+            std::cout << "  Hardware not available, skipping...\n";
+            continue;
+        }
+        
+        auto start_time = std::chrono::high_resolution_clock::now();
+        
+        // Create vector of initial conditions
+        std::vector<std::vector<double>> states(num_integrations, initial_state);
+        
+        // Parallel integration using the unified interface
+        parallel_facade->parallel_for_each(states.begin(), states.end(), [&](auto& state) {
+            auto integrator = diffeq::ode::factory::make_rk4_integrator<std::vector<double>, double>(system);
+            
+            double t = 0.0;
+            while (t < end_time) {
+                double step_size = std::min(dt, end_time - t);
+                integrator->step(state, step_size);
+                t += step_size;
+            }
+        });
+        
+        auto end_time_chrono = std::chrono::high_resolution_clock::now();
+        auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time_chrono - start_time);
+        
+        // Verify results (should be approximately e^(-k*t) = e^(-1) ≈ 0.368)
+        double expected_result = std::exp(-system.k * end_time);
+        double mean_result = 0.0;
+        for (const auto& state : states) {
+            mean_result += state[0];
+        }
+        mean_result /= num_integrations;
+        
+        double error = std::abs(mean_result - expected_result);
+        
+        std::cout << "  Execution time: " << duration.count() << "ms\n";
+        std::cout << "  Throughput: " << (num_integrations * 1000.0) / duration.count() << " integrations/second\n";
+        std::cout << "  Average result: " << mean_result << " (expected: " << expected_result << ")\n";
+        std::cout << "  Error: " << error << "\n";
+        std::cout << "  Max concurrency: " << parallel_facade->get_max_concurrency() << "\n";
+    }
+}
+
+} // namespace multi_hardware_demo
+
+/**
+ * @brief Demonstration function that runs all examples
+ */
+void demonstrate_all_parallelism_features() {
+    std::cout << "=== Enhanced Parallelism Capabilities Demo ===\n";
+    std::cout << "Demonstrating modern C++ parallelism with unified hardware interface\n";
+    
+    // Run robotics control example
+    robotics_control::demonstrate_realtime_control();
+    
+    // Run stochastic research example
+    stochastic_research::demonstrate_monte_carlo_simulation();
+    
+    // Run multi-hardware benchmark
+    multi_hardware_demo::benchmark_hardware_targets();
+    
+    std::cout << "\n=== All Parallelism Demonstrations Complete ===\n";
+}
+
+} // namespace diffeq::examples::parallelism