A framework to test cache replacement policies.
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Updated
Mar 22, 2017 - Java
A framework to test cache replacement policies.
Implemented modern last-level cache(LLC) with the concept of "Perceptron Learning for Reuse Prediction" that use neural network idea, which is training the predictor by a smaller independent cache with a series of features.
ARC Cache Implementation (in Haskell!)
Using Belady's algorithm for improved cache replacement
Source code for the cache replacement policy published in [Faldu et al., PACT'17] and [Faldu et al., CRC2'17].
Source code for the cache replacement policy published in [Faldu et al., PACT'17] and [Faldu et al., CRC2'17].
A project for Advanced Operating System(CS604) that implements ARC cache replacement policy.
The PyTorch codebase for -> DEAP Cache: Deep Eviction Admission and Prefetching for Cache.
A console application to simulate cache and cache replacement policies : LRU, LFU and MRU
Programs implemented during lab sessions of course CSC403 i.e (Computer Organization & Architecture)
Manageable LRU in-memory cache instance for fast lookups and configurable eviction policies
🧠 Implementation of two cache replacement and two memory scheduling algorithms.
Implementation of Cache Replacement Algorithm (FIFO and LRU) and calculation of corresponding Cache Hit Ratio.
A transformer-based cache replacement model for CDNs using imitation learning technique with Belady's optimal policy
A comprehensive collection of cache eviction policies implemented in python, providing practical examples for Least Recently Used (LRU), Least Frequently Used (LFU), and other strategies to optimize data caching in your applications.
Trace-driven cache memory simulator with LRU, MRU, RR and Belady replacement policies.
Cache replacement algorithms, including Least Recently Used (LRU), First In First Out (FIFO), and Enhanced Not Recently Used (ENRU).
High performing caching package for node/javascript
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