"""
CPU Driver for true parallel execution using process pools and thread pools.
Implements non-blocking, concurrent execution for maximum performance.
"""

import concurrent.futures
import multiprocessing as mp
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import numpy as np
import time
from virtual_cpu import CPU, Core, THREADS_PER_CORE

class ParallelExecutor:
    """Manages parallel execution of instructions using process and thread pools"""
    def __init__(self, thread_id, core_id, shared_memory):
        self.thread_id = thread_id
        self.core_id = core_id
        self.shared_memory = shared_memory
        self._setup_simd()
        
    def _setup_simd(self):
        """Set up SIMD acceleration using NumPy"""
        # Enable hardware acceleration
        try:
            import mkl
            mkl.set_num_threads(THREADS_PER_CORE)
        except ImportError:
            pass
        
    def execute(self, instruction):
        """Execute instruction with SIMD acceleration where possible"""
        if instruction['type'] in ['VADD', 'VMUL', 'VDOT']:
            return self._execute_simd(instruction)
        return self._execute_scalar(instruction)
                
    def _execute_instruction(self, instruction):
        # Simulate instruction execution with realistic timing
        start_time = time.time()
        # Execute the actual instruction (will be implemented with real CPU instructions)
        time.sleep(0.001)  # Simulate instruction latency
        return {
            'execution_time': time.time() - start_time,
            'instruction': instruction,
            'status': 'completed'
        }

class ParallelCore:
    """Manages a CPU core with parallel thread execution using thread pools"""
    def __init__(self, core_id, shared_memory, vector_memory):
        self.core_id = core_id
        self.shared_memory = shared_memory
        self.vector_memory = vector_memory
        self.thread_pool = ThreadPoolExecutor(max_workers=THREADS_PER_CORE)
        self.executors = [
            ParallelExecutor(i, core_id, shared_memory) 
            for i in range(THREADS_PER_CORE)
        ]
        # Enable SIMD acceleration
        self.vector_view = np.frombuffer(vector_memory, dtype=np.float32)
        
    def execute_parallel(self, instructions):
        """Execute multiple instructions in parallel using thread pool"""
        futures = []
        for instr in instructions:
            executor = self.executors[instr['thread_id']]
            futures.append(
                self.thread_pool.submit(executor.execute, instr)
            )
            
        # Main core execution loop
        while self.running:
            try:
                for i, q in enumerate(self.instruction_queues):
                    if not q.empty():
                        instruction = q.get_nowait()
                        if instruction is None:  # Shutdown signal
                            self.shutdown()
                            return
                        # Distribute instruction to thread
                        thread_queues[i % THREADS_PER_CORE].put(instruction)
            except queue.Empty:
                continue
                
    def shutdown(self):
        self.running = False
        for thread in self.threads:
            thread.running = False
        for thread in self.threads:
            thread.join()

class CPUDriver:
    """Main CPU driver for true parallel execution using shared memory and SIMD"""
    def __init__(self, num_cores=4):
        self.num_cores = num_cores
        
        # Create shared memory for all cores
        self.shared_mem_size = 1024*1024  # 1MB
        self.shared_memory = mp.RawArray('f', self.shared_mem_size)
        
        # SIMD setup for vector operations
        self.vector_size = 256
        self.vector_units = mp.RawArray('f', self.vector_size * num_cores)
        
        # Create process pool context
        self.ctx = mp.get_context('spawn')
        self.process_pool = self.ctx.Pool(processes=num_cores)
        
        # Initialize cores with direct memory access
        self.cores = []
        for i in range(num_cores):
            core = ParallelCore(
                i, 
                self.shared_memory, 
                self.vector_units[i*self.vector_size:(i+1)*self.vector_size]
            )
            self.cores.append(core)
            
    def execute_batch(self, instructions):
        """Execute instructions in parallel using direct memory access"""
        # Split instructions by core
        core_batches = [[] for _ in range(self.num_cores)]
        for instr in instructions:
            core_batches[instr['core_id']].append(instr)
            
        # Start all cores in parallel
        async_results = []
        for core_id, batch in enumerate(core_batches):
            if batch:
                result = self.process_pool.apply_async(
                    self.cores[core_id].execute_parallel,
                    (batch,)
                )
                async_results.append(result)
                
        # Execute SIMD operations directly
        self._parallel_simd_execution([
            i for i in instructions 
            if i['type'].startswith('V')
        ])
        
        return async_results
    
    def _execute_simd_batch(self, instructions):
        """Execute vector operations using hardware SIMD"""
        # Group by operation type
        vadd_ops = []
        vmul_ops = []
        vdot_ops = []
        
        for instr in instructions:
            if instr['type'] == 'VADD':
                vadd_ops.append(instr)
            elif instr['type'] == 'VMUL':
                vmul_ops.append(instr)
            elif instr['type'] == 'VDOT':
                vdot_ops.append(instr)
                
        # Execute in parallel using NumPy's optimized functions
        if vadd_ops:
            self._parallel_vadd(vadd_ops)
        if vmul_ops:
            self._parallel_vmul(vmul_ops)
        if vdot_ops:
            self._parallel_vdot(vdot_ops)
            
    def _parallel_vadd(self, operations):
        """Execute vector adds in parallel using SIMD"""
        vectors = [op['vectors'] for op in operations]
        result = np.add.reduce(vectors, axis=0)
        for i, op in enumerate(operations):
            self.memory_view[op['dest']:op['dest']+len(result)] = result

    def distribute_program(self, program):
        """Distribute a program's instructions across cores and threads"""
        core_id = 0
        thread_id = 0
        
        for instruction in program:
            self.submit_instruction(core_id, thread_id, instruction)
            
            # Round-robin distribution across cores and threads
            thread_id = (thread_id + 1) % THREADS_PER_CORE
            if thread_id == 0:
                core_id = (core_id + 1) % self.num_cores