Python – LRU Cache

05 May, 2020
LRU Cache is the least recently used cache which is basically used for Memory Organization. In this, the elements come as First in First Out format. We are given total possible page numbers that can be referred to. We are also given cache (or memory) size (Number of page frames that cache can hold at a time). The LRU caching scheme is to remove the least recently used frame when the cache is full and a new page is referenced which is not there in the cache. There are generally two terms use with LRU Cache, let’s see them –
Page hit: If the required page is found in the main memory then it is a page hit.
Page Fault: If the required page is not found in the main memory then page fault occurs.
When a page is referenced, the required page may be in the memory. If it is in the memory, we need to detach the node of the list and bring it to the front of the queue.If the required page is not in memory, we bring that in memory. In simple words, we add a new node to the front of the queue and update the corresponding node address in the hash. If the queue is full, i.e. all the frames are full, we remove a node from the rear of the queue, and add the new node to the front of the queue.
Example – Consider the following reference string :
1, 2, 3, 4, 1, 2, 5, 1, 2, 3, 4, 5
Find the number of page faults using least recently used (LRU) page replacement algorithm with 3 page frames.Explanation –
LRU Cache Using PythonYou can implement this with the help of the queue. In this, we have used Queue using the linked list. Run the given code in Pycharm IDE.
import time         class Node:          # Nodes are represented in n    def __init__(self, key, val):        self.key = key        self.val = val        self.next = None        self.prev = None      class LRUCache:    cache_limit = None          # if the DEBUG is TRUE then it    # will execute    DEBUG = False       def __init__(self, func):        self.func = func        self.cache = {}        self.head = Node(0, 0)        self.tail = Node(0, 0)        self.head.next = self.tail        self.tail.prev = self.head       def __call__(self, *args, **kwargs):                  # The cache presents with the help        # of Linked List        if args in self.cache:            self.llist(args)                          if self.DEBUG == True:                return f'Cached...{args}\n{self.cache[args]}\nCache: {self.cache}'            return self.cache[args]           # The given cache keeps on moving.        if self.cache_limit is not None:                          if len(self.cache) > self.cache_limit:                n = self.head.next                self._remove(n)                del self.cache[n.key]           # Compute and cache and node to see whether         # the following element is present or not         # based on the given input.        result = self.func(*args, **kwargs)        self.cache[args] = result        node = Node(args, result)        self._add(node)                  if self.DEBUG == True:            return f'{result}\nCache: {self.cache}'        return result       # Remove from double linked-list - Node.    def _remove(self, node):        p = node.prev        n = node.next        p.next = n        n.prev = p       # Add to double linked-list - Node.    def _add(self, node):        p = self.tail.prev        p.next = node        self.tail.prev = node        node.prev = p        node.next = self.tail       # Over here the result task is being done     def llist(self, args):        current = self.head                  while True:                          if current.key == args:                node = current                self._remove(node)                self._add(node)                                  if self.DEBUG == True:                    del self.cache[node.key]                      self.cache[node.key] = node.val                 break                          else:                current = current.next      # Default Debugging is FALSE. For # execution of DEBUG is set to TRUELRUCache.DEBUG = True   # The DEFAULT test limit is NONE.LRUCache.cache_limit = 3     @LRUCachedef ex_func_01(n):    print(f'Computing...{n}')    time.sleep(1)    return n      print(f'\nFunction: ex_func_01')print(ex_func_01(1))print(ex_func_01(2))print(ex_func_01(3))print(ex_func_01(4))print(ex_func_01(1))print(ex_func_01(2))print(ex_func_01(5))print(ex_func_01(1))print(ex_func_01(2))print(ex_func_01(3))print(ex_func_01(4))print(ex_func_01(5))
Output:
Function: ex_func_01
Computing...1
1
Cache: {(1,): 1}
Computing...2
2
Cache: {(1,): 1, (2,): 2}
Computing...3
3
Cache: {(1,): 1, (2,): 2, (3,): 3}
Computing...4
4
Cache: {(1,): 1, (2,): 2, (3,): 3, (4,): 4}
Cached...(1,)
1
Cache: {(2,): 2, (3,): 3, (4,): 4, (1,): 1}
Cached...(2,)
2
Cache: {(3,): 3, (4,): 4, (1,): 1, (2,): 2}
Computing...5
5
Cache: {(4,): 4, (1,): 1, (2,): 2, (5,): 5}
Cached...(1,)
1
Cache: {(4,): 4, (2,): 2, (5,): 5, (1,): 1}
Cached...(2,)
2
Cache: {(4,): 4, (5,): 5, (1,): 1, (2,): 2}
Computing...3
3
Cache: {(5,): 5, (1,): 1, (2,): 2, (3,): 3}
Computing...4
4
Cache: {(1,): 1, (2,): 2, (3,): 3, (4,): 4}
Computing...5
5
Cache: {(2,): 2, (3,): 3, (4,): 4, (5,): 5}
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