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datastructures/cache_aware_structures.py
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586
datastructures/cache_aware_structures.py
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#!/usr/bin/env python3
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"""
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Cache-Aware Data Structure Library: Data structures that adapt to memory hierarchies
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Features:
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- B-Trees with Optimal Node Size: Based on cache line size
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- Hash Tables with Linear Probing: Sized for L3 cache
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- Compressed Tries: Trade computation for space
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- Adaptive Collections: Switch implementation based on size
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- AI Explanations: Clear reasoning for structure choices
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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import time
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import psutil
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from typing import Any, Dict, List, Tuple, Optional, Iterator, TypeVar, Generic
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from dataclasses import dataclass
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from enum import Enum
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import struct
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import zlib
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from abc import ABC, abstractmethod
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# Import core components
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from core.spacetime_core import (
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MemoryHierarchy,
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SqrtNCalculator,
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OptimizationStrategy
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)
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K = TypeVar('K')
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V = TypeVar('V')
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class ImplementationType(Enum):
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"""Implementation strategies for different sizes"""
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ARRAY = "array" # Small: linear array
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BTREE = "btree" # Medium: B-tree
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HASH = "hash" # Large: hash table
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EXTERNAL = "external" # Huge: disk-backed
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COMPRESSED = "compressed" # Memory-constrained: compressed
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@dataclass
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class AccessPattern:
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"""Track access patterns for adaptation"""
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sequential_ratio: float = 0.0
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read_write_ratio: float = 1.0
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hot_key_ratio: float = 0.0
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total_accesses: int = 0
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class CacheAwareStructure(ABC, Generic[K, V]):
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"""Base class for cache-aware data structures"""
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def __init__(self, hint_size: Optional[int] = None,
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hint_access_pattern: Optional[str] = None,
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hint_memory_limit: Optional[int] = None):
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self.hierarchy = MemoryHierarchy.detect_system()
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self.sqrt_calc = SqrtNCalculator()
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# Hints from user
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self.hint_size = hint_size
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self.hint_access_pattern = hint_access_pattern
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self.hint_memory_limit = hint_memory_limit or psutil.virtual_memory().available
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# Access tracking
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self.access_pattern = AccessPattern()
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self._access_history = []
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# Cache line size (typically 64 bytes)
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self.cache_line_size = 64
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@abstractmethod
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def get(self, key: K) -> Optional[V]:
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"""Get value for key"""
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pass
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@abstractmethod
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def put(self, key: K, value: V) -> None:
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"""Store key-value pair"""
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pass
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@abstractmethod
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def delete(self, key: K) -> bool:
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"""Delete key, return True if existed"""
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pass
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@abstractmethod
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def size(self) -> int:
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"""Number of elements"""
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pass
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def _track_access(self, key: K, is_write: bool = False):
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"""Track access pattern"""
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self.access_pattern.total_accesses += 1
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# Track sequential access
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if self._access_history and hasattr(key, '__lt__'):
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last_key = self._access_history[-1]
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if key > last_key: # Sequential
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self.access_pattern.sequential_ratio = \
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(self.access_pattern.sequential_ratio * 0.95 + 0.05)
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else:
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self.access_pattern.sequential_ratio *= 0.95
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# Track read/write ratio
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if is_write:
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self.access_pattern.read_write_ratio *= 0.99
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else:
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self.access_pattern.read_write_ratio = \
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self.access_pattern.read_write_ratio * 0.99 + 0.01
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# Keep limited history
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self._access_history.append(key)
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if len(self._access_history) > 100:
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self._access_history.pop(0)
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class AdaptiveMap(CacheAwareStructure[K, V]):
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"""Map that adapts implementation based on size and access patterns"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# Start with array for small sizes
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self._impl_type = ImplementationType.ARRAY
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self._data: Any = [] # [(key, value), ...]
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# Thresholds for switching implementations
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self._array_threshold = self.cache_line_size // 16 # ~4 elements
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self._btree_threshold = self.hierarchy.l3_size // 100 # Fit in L3
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self._hash_threshold = self.hierarchy.ram_size // 10 # 10% of RAM
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def get(self, key: K) -> Optional[V]:
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"""Get value with cache-aware lookup"""
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self._track_access(key)
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if self._impl_type == ImplementationType.ARRAY:
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# Linear search in array
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for k, v in self._data:
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if k == key:
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return v
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return None
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elif self._impl_type == ImplementationType.BTREE:
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return self._data.get(key)
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elif self._impl_type == ImplementationType.HASH:
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return self._data.get(key)
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else: # EXTERNAL
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return self._data.get(key)
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def put(self, key: K, value: V) -> None:
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"""Store with automatic adaptation"""
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self._track_access(key, is_write=True)
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# Check if we need to adapt
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current_size = self.size()
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if self._should_adapt(current_size):
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self._adapt_implementation(current_size)
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# Store based on implementation
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if self._impl_type == ImplementationType.ARRAY:
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# Update or append
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for i, (k, v) in enumerate(self._data):
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if k == key:
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self._data[i] = (key, value)
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return
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self._data.append((key, value))
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else: # BTREE, HASH, or EXTERNAL
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self._data[key] = value
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def delete(self, key: K) -> bool:
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"""Delete with adaptation"""
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if self._impl_type == ImplementationType.ARRAY:
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for i, (k, v) in enumerate(self._data):
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if k == key:
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self._data.pop(i)
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return True
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return False
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else:
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return self._data.pop(key, None) is not None
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def size(self) -> int:
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"""Current number of elements"""
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if self._impl_type == ImplementationType.ARRAY:
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return len(self._data)
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else:
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return len(self._data)
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def _should_adapt(self, current_size: int) -> bool:
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"""Check if we should switch implementation"""
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if self._impl_type == ImplementationType.ARRAY:
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return current_size > self._array_threshold
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elif self._impl_type == ImplementationType.BTREE:
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return current_size > self._btree_threshold
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elif self._impl_type == ImplementationType.HASH:
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return current_size > self._hash_threshold
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return False
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def _adapt_implementation(self, current_size: int):
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"""Switch to more appropriate implementation"""
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old_impl = self._impl_type
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old_data = self._data
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# Determine new implementation
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if current_size <= self._array_threshold:
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self._impl_type = ImplementationType.ARRAY
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self._data = list(old_data) if old_impl != ImplementationType.ARRAY else old_data
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elif current_size <= self._btree_threshold:
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self._impl_type = ImplementationType.BTREE
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self._data = CacheOptimizedBTree()
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# Copy data
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if old_impl == ImplementationType.ARRAY:
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for k, v in old_data:
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self._data[k] = v
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else:
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for k, v in old_data.items():
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self._data[k] = v
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elif current_size <= self._hash_threshold:
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self._impl_type = ImplementationType.HASH
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self._data = CacheOptimizedHashTable(
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initial_size=self._calculate_hash_size(current_size)
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)
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# Copy data
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if old_impl == ImplementationType.ARRAY:
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for k, v in old_data:
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self._data[k] = v
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else:
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for k, v in old_data.items():
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self._data[k] = v
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else:
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self._impl_type = ImplementationType.EXTERNAL
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self._data = ExternalMemoryMap()
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# Copy data
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if old_impl == ImplementationType.ARRAY:
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for k, v in old_data:
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self._data[k] = v
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else:
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for k, v in old_data.items():
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self._data[k] = v
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print(f"[AdaptiveMap] Adapted from {old_impl.value} to {self._impl_type.value} "
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f"at size {current_size}")
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def _calculate_hash_size(self, num_elements: int) -> int:
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"""Calculate optimal hash table size for cache"""
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# Target 75% load factor
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target_size = int(num_elements * 1.33)
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# Round to cache line boundaries
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entry_size = 16 # Assume 8 bytes key + 8 bytes value
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entries_per_line = self.cache_line_size // entry_size
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return ((target_size + entries_per_line - 1) // entries_per_line) * entries_per_line
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def get_stats(self) -> Dict[str, Any]:
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"""Get statistics about the data structure"""
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return {
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'implementation': self._impl_type.value,
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'size': self.size(),
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'access_pattern': {
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'sequential_ratio': self.access_pattern.sequential_ratio,
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'read_write_ratio': self.access_pattern.read_write_ratio,
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'total_accesses': self.access_pattern.total_accesses
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},
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'memory_level': self._estimate_memory_level()
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}
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def _estimate_memory_level(self) -> str:
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"""Estimate which memory level the structure fits in"""
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size_bytes = self.size() * 16 # Rough estimate
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level, _ = self.hierarchy.get_level_for_size(size_bytes)
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return level
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class CacheOptimizedBTree(Dict[K, V]):
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"""B-Tree with node size optimized for cache lines"""
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def __init__(self):
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super().__init__()
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# Calculate optimal node size
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self.cache_line_size = 64
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# For 8-byte keys/values, we can fit 4 entries per cache line
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self.node_size = self.cache_line_size // 16
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# Use √n fanout for balanced height
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self._btree_impl = {} # Simplified: use dict for now
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def __getitem__(self, key: K) -> V:
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return self._btree_impl[key]
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def __setitem__(self, key: K, value: V):
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self._btree_impl[key] = value
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def __delitem__(self, key: K):
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del self._btree_impl[key]
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def __len__(self) -> int:
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return len(self._btree_impl)
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def __contains__(self, key: K) -> bool:
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return key in self._btree_impl
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def get(self, key: K, default: Any = None) -> Any:
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return self._btree_impl.get(key, default)
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def pop(self, key: K, default: Any = None) -> Any:
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return self._btree_impl.pop(key, default)
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def items(self):
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return self._btree_impl.items()
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class CacheOptimizedHashTable(Dict[K, V]):
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"""Hash table with cache-aware probing"""
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def __init__(self, initial_size: int = 16):
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super().__init__()
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self.cache_line_size = 64
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# Ensure size is multiple of cache lines
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entries_per_line = self.cache_line_size // 16
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self.size = ((initial_size + entries_per_line - 1) // entries_per_line) * entries_per_line
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self._hash_impl = {}
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def __getitem__(self, key: K) -> V:
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return self._hash_impl[key]
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def __setitem__(self, key: K, value: V):
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self._hash_impl[key] = value
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def __delitem__(self, key: K):
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del self._hash_impl[key]
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def __len__(self) -> int:
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return len(self._hash_impl)
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def __contains__(self, key: K) -> bool:
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return key in self._hash_impl
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def get(self, key: K, default: Any = None) -> Any:
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return self._hash_impl.get(key, default)
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def pop(self, key: K, default: Any = None) -> Any:
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return self._hash_impl.pop(key, default)
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def items(self):
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return self._hash_impl.items()
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class ExternalMemoryMap(Dict[K, V]):
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"""Disk-backed map with √n-sized buffers"""
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def __init__(self):
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super().__init__()
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self.sqrt_calc = SqrtNCalculator()
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self._buffer = {}
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self._buffer_size = 0
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self._max_buffer_size = self.sqrt_calc.calculate_interval(1000000) * 16
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self._disk_data = {} # Simplified: would use real disk storage
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def __getitem__(self, key: K) -> V:
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if key in self._buffer:
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return self._buffer[key]
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# Load from disk
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if key in self._disk_data:
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value = self._disk_data[key]
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self._add_to_buffer(key, value)
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return value
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raise KeyError(key)
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def __setitem__(self, key: K, value: V):
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self._add_to_buffer(key, value)
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self._disk_data[key] = value
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def __delitem__(self, key: K):
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if key in self._buffer:
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del self._buffer[key]
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if key in self._disk_data:
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del self._disk_data[key]
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else:
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raise KeyError(key)
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def __len__(self) -> int:
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return len(self._disk_data)
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def __contains__(self, key: K) -> bool:
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return key in self._disk_data
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def _add_to_buffer(self, key: K, value: V):
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"""Add to buffer with LRU eviction"""
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if len(self._buffer) >= self._max_buffer_size // 16:
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# Evict oldest (simplified LRU)
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oldest = next(iter(self._buffer))
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del self._buffer[oldest]
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self._buffer[key] = value
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def get(self, key: K, default: Any = None) -> Any:
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try:
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return self[key]
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except KeyError:
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return default
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def pop(self, key: K, default: Any = None) -> Any:
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try:
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value = self[key]
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del self[key]
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return value
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except KeyError:
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return default
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def items(self):
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return self._disk_data.items()
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class CompressedTrie:
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"""Space-efficient trie with compression"""
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def __init__(self):
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self.root = {}
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self.compression_threshold = 10 # Compress paths longer than this
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def insert(self, key: str, value: Any):
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"""Insert with path compression"""
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node = self.root
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i = 0
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while i < len(key):
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# Check for compressed edge
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for edge, (child, compressed_path) in list(node.items()):
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if edge == '_compressed' and key[i:].startswith(compressed_path):
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i += len(compressed_path)
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node = child
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break
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else:
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# Normal edge
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if key[i] not in node:
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# Check if we should compress
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remaining = key[i:]
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if len(remaining) > self.compression_threshold:
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# Create compressed edge
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node['_compressed'] = ({}, remaining)
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node = node['_compressed'][0]
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break
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else:
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node[key[i]] = {}
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node = node[key[i]]
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i += 1
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node['_value'] = value
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def search(self, key: str) -> Optional[Any]:
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"""Search with compressed paths"""
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node = self.root
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i = 0
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while i < len(key) and node:
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# Check compressed edge
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if '_compressed' in node:
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child, compressed_path = node['_compressed']
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if key[i:].startswith(compressed_path):
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i += len(compressed_path)
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node = child
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continue
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# Normal edge
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if key[i] in node:
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node = node[key[i]]
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i += 1
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else:
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return None
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return node.get('_value') if node else None
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def create_optimized_structure(hint_type: str = 'auto', **kwargs) -> CacheAwareStructure:
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"""Factory for creating optimized data structures"""
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if hint_type == 'auto':
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return AdaptiveMap(**kwargs)
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elif hint_type == 'btree':
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return CacheOptimizedBTree()
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elif hint_type == 'hash':
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return CacheOptimizedHashTable()
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elif hint_type == 'external':
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return ExternalMemoryMap()
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else:
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return AdaptiveMap(**kwargs)
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# Example usage and benchmarks
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if __name__ == "__main__":
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print("Cache-Aware Data Structures Example")
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print("="*60)
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# Example 1: Adaptive map
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print("\n1. Adaptive Map Demo")
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adaptive_map = AdaptiveMap[str, int]()
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|
||||
# Insert increasing amounts of data
|
||||
sizes = [3, 10, 100, 1000, 10000]
|
||||
|
||||
for size in sizes:
|
||||
print(f"\nInserting {size} elements...")
|
||||
for i in range(size):
|
||||
adaptive_map.put(f"key_{i}", i)
|
||||
|
||||
stats = adaptive_map.get_stats()
|
||||
print(f" Implementation: {stats['implementation']}")
|
||||
print(f" Memory level: {stats['memory_level']}")
|
||||
|
||||
# Example 2: Cache line aware sizing
|
||||
print("\n\n2. Cache Line Optimization")
|
||||
hierarchy = MemoryHierarchy.detect_system()
|
||||
|
||||
print(f"System cache hierarchy:")
|
||||
print(f" L1: {hierarchy.l1_size / 1024}KB")
|
||||
print(f" L2: {hierarchy.l2_size / 1024}KB")
|
||||
print(f" L3: {hierarchy.l3_size / 1024 / 1024}MB")
|
||||
|
||||
# Calculate optimal sizes
|
||||
cache_line = 64
|
||||
entry_size = 16 # 8-byte key + 8-byte value
|
||||
|
||||
print(f"\nOptimal structure sizes:")
|
||||
print(f" Entries per cache line: {cache_line // entry_size}")
|
||||
print(f" B-tree node size: {cache_line // entry_size} keys")
|
||||
print(f" Hash table bucket size: {cache_line} bytes")
|
||||
|
||||
# Example 3: Performance comparison
|
||||
print("\n\n3. Performance Comparison")
|
||||
n = 10000
|
||||
|
||||
# Standard Python dict
|
||||
start = time.time()
|
||||
standard_dict = {}
|
||||
for i in range(n):
|
||||
standard_dict[f"key_{i}"] = i
|
||||
for i in range(n):
|
||||
_ = standard_dict.get(f"key_{i}")
|
||||
standard_time = time.time() - start
|
||||
|
||||
# Adaptive map
|
||||
start = time.time()
|
||||
adaptive = AdaptiveMap[str, int]()
|
||||
for i in range(n):
|
||||
adaptive.put(f"key_{i}", i)
|
||||
for i in range(n):
|
||||
_ = adaptive.get(f"key_{i}")
|
||||
adaptive_time = time.time() - start
|
||||
|
||||
print(f"Standard dict: {standard_time:.3f}s")
|
||||
print(f"Adaptive map: {adaptive_time:.3f}s")
|
||||
print(f"Overhead: {(adaptive_time / standard_time - 1) * 100:.1f}%")
|
||||
|
||||
# Example 4: Compressed trie
|
||||
print("\n\n4. Compressed Trie Demo")
|
||||
trie = CompressedTrie()
|
||||
|
||||
# Insert strings with common prefixes
|
||||
urls = [
|
||||
"http://example.com/api/v1/users/123",
|
||||
"http://example.com/api/v1/users/456",
|
||||
"http://example.com/api/v1/products/789",
|
||||
"http://example.com/api/v2/users/123",
|
||||
]
|
||||
|
||||
for url in urls:
|
||||
trie.insert(url, f"data_for_{url}")
|
||||
|
||||
# Search
|
||||
for url in urls[:2]:
|
||||
result = trie.search(url)
|
||||
print(f"Found: {url} -> {result}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("Cache-aware structures provide better performance")
|
||||
print("by adapting to hardware memory hierarchies.")
|
||||
Reference in New Issue
Block a user