### 连续内存回收算法的实现与外部碎片率计算 在 Python 中，可以模拟连续内存管理机制，并实现一种简单的内存回收算法。以下代码示例展示了如何模拟块式内存管理[^4]、计算外部碎片率以及评估合并效率的提升。 #### 1. 内存块类定义 首先定义一个内存块类 `MemoryBlock`，用于表示每个内存块的状态（已分配或空闲）和大小。 ```python class MemoryBlock: def __init__(self, size, is_allocated=False): self.size = size self.is_allocated = is_allocated def allocate(self): self.is_allocated = True def free(self): self.is_allocated = False ``` #### 2. 内存管理器类 接下来定义一个内存管理器类 `MemoryManager`，用于管理所有内存块，执行分配、释放操作，并计算外部碎片率和合并效率。 ```python class MemoryManager: def __init__(self, total_memory_size, block_size): self.total_memory_size = total_memory_size self.block_size = block_size self.blocks = [MemoryBlock(block_size) for _ in range(total_memory_size // block_size)] def allocate_memory(self, required_size): allocated_blocks = [] for i, block in enumerate(self.blocks): if not block.is_allocated and len(allocated_blocks) < required_size // self.block_size: block.allocate() allocated_blocks.append(i) return allocated_blocks def free_memory(self, block_indices): for idx in block_indices: if 0 <= idx < len(self.blocks): self.blocks[idx].free() def calculate_external_fragmentation(self): total_free_memory = sum(block.size for block in self.blocks if not block.is_allocated) largest_contiguous_free_memory = 0 current_contiguous_free_memory = 0 for block in self.blocks: if not block.is_allocated: current_contiguous_free_memory += block.size largest_contiguous_free_memory = max(largest_contiguous_free_memory, current_contiguous_free_memory) else: current_contiguous_free_memory = 0 external_fragmentation = (total_free_memory - largest_contiguous_free_memory) / self.total_memory_size * 100 return external_fragmentation def merge_blocks(self): merged_blocks = [] current_block_size = 0 current_is_allocated = None for block in self.blocks: if current_is_allocated is None or block.is_allocated == current_is_allocated: current_block_size += block.size current_is_allocated = block.is_allocated else: merged_blocks.append(MemoryBlock(current_block_size, is_allocated=current_is_allocated)) current_block_size = block.size current_is_allocated = block.is_allocated # Add the last block if current_block_size > 0: merged_blocks.append(MemoryBlock(current_block_size, is_allocated=current_is_allocated)) self.blocks = merged_blocks def calculate_merge_efficiency(self): initial_fragmentation = self.calculate_external_fragmentation() self.merge_blocks() final_fragmentation = self.calculate_external_fragmentation() efficiency = (initial_fragmentation - final_fragmentation) / initial_fragmentation * 100 if initial_fragmentation != 0 else 0 return efficiency ``` #### 3. 测试代码 以下是一个测试用例，展示如何使用上述类来模拟内存分配、释放、外部碎片率计算以及合并效率的提升。 ```python # 初始化内存管理器 memory_manager = MemoryManager(total_memory_size=1024, block_size=64) # 分配内存 print("Allocating memory...") allocated_blocks_1 = memory_manager.allocate_memory(required_size=128) allocated_blocks_2 = memory_manager.allocate_memory(required_size=256) # 计算初始外部碎片率 external_fragmentation_initial = memory_manager.calculate_external_fragmentation() print(f"Initial External Fragmentation: {external_fragmentation_initial:.2f}%") # 释放部分内存 print("Freeing memory...") memory_manager.free_memory(block_indices=allocated_blocks_1) # 计算合并前后的外部碎片率和合并效率 merge_efficiency = memory_manager.calculate_merge_efficiency() external_fragmentation_final = memory_manager.calculate_external_fragmentation() print(f"Final External Fragmentation after merging: {external_fragmentation_final:.2f}%") print(f"Merge Efficiency Improvement: {merge_efficiency:.2f}%") ``` ### 解释 - **内存块分配**：通过 `allocate_memory` 方法模拟内存分配，返回分配的块索引。 - **内存释放**：通过 `free_memory` 方法释放指定的内存块。 - **外部碎片率计算**：通过 `calculate_external_fragmentation` 方法计算当前外部碎片率。 - **内存合并**：通过 `merge_blocks` 方法将相邻的空闲块合并为更大的块，减少外部碎片。 - **合并效率评估**：通过 `calculate_merge_efficiency` 方法评估合并后外部碎片率的改善程度。 ###