On the Theory of Spatial and Temporal Locality

This paper studies the theory of caching and temporal and spatial locality. We show the following results: (1) hashing can be used to guarantee that caches with limited associativity behave as well as fully associative cache; (2) temporal locality cannot be characterized using one, or few parameters; (3) temporal locality and spatial locality cannot be studied separately; and (4) unlike temporal locality, spatial locality cannot be managed efficiently online

Learning-Based Optimization of Cache Content in a Small Cell Base Station

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.Comment: Accepted to IEEE ICC 2014, Sydney, Australia. Minor typos corrected. Algorithm MCUCB correcte

Improving Cache Hits On Replacment Blocks Using Weighted LRU-LFU Combinations

Block replacement refers to the process of selecting a block of data or a cache line to be evicted or replaced when a new block needs to be brought into a cache or a memory hierarchy. In computer systems, block replacement policies are used in caching mechanisms, such as in CPU caches or disk caches, to determine which blocks are evicted when the cache is full and new data needs to be fetched. The combination of LRU (Least Recently Used) and LFU (Least Frequently Used) in a weighted manner is known as the "LFU2" algorithm. LFU2 is an enhanced caching algorithm that aims to leverage the benefits of both LRU and LFU by considering both recency and frequency of item access. In LFU2, each item in the cache is associated with two counters: the usage counter and the recency counter. The usage counter tracks the frequency of item access, while the recency counter tracks the recency of item access. These counters are used to calculate a combined weight for each item in the cache. Based on the experimental results, the LRU-LFU combination method succeeded in increasing cache hits from 94.8% on LFU and 95.5% on LFU to 96.6%

Software-Based Self-Test of Set-Associative Cache Memories

Embedded microprocessor cache memories suffer from limited observability and controllability creating problems during in-system tests. This paper presents a procedure to transform traditional march tests into software-based self-test programs for set-associative cache memories with LRU replacement. Among all the different cache blocks in a microprocessor, testing instruction caches represents a major challenge due to limitations in two areas: 1) test patterns which must be composed of valid instruction opcodes and 2) test result observability: the results can only be observed through the results of executed instructions. For these reasons, the proposed methodology will concentrate on the implementation of test programs for instruction caches. The main contribution of this work lies in the possibility of applying state-of-the-art memory test algorithms to embedded cache memories without introducing any hardware or performance overheads and guaranteeing the detection of typical faults arising in nanometer CMOS technologie

LRU-PEA: A smart replacement policy for non-uniform cache architectures on chip multiprocessors

The increasing speed-gap between processor and memory and the limited memory bandwidth make last-level cache performance crucial for CMP architectures. Non Uniform Cache Architectures (NUCA) has been introduced to deal with this problem. This memory organization divides the whole memory space into smaller pieces or banks allowing nearer banks to have better access latencies than further banks.Moreover, an adaptive replacement policy that efficiently reduces misses in the last-level cache could boost performance, particularly if set associativity is assumed. Unfortunately, traditional replacement policies do not behave properly as they were assumed for single-processors. This paper focuses on Bank Replacement. This policy involves three key decisions when there is a miss: where to place a data within the cache set, which data to evict from the cache set and finally, where to place the evicted data. We propose a novel replacement technique that enables more intelligent replacement decisions to be taken, based on the observation that some type of data are less commonly accessed depending of the bank where they reside. We call this technique as LRU-PEA (Least Recently Used with a Priority Eviction Approach). We show that the proposed technique significantly reduces the requests to the off-chip memory by increasing the hit ratio in the NUCA cache. This translates into an average IPC improvement of 8% and into an Energy per Instruction (EPI) reduction of 5%.Preprin

Interpretability of AI in Computer Systems and Public Policy

Advances in Artificial Intelligence (AI) have led to spectacular innovations and sophisticated systems for tasks that were thought to be capable only by humans. Examples include playing chess and Go, face and voice recognition, driving vehicles, and more. In recent years, the impact of AI has moved beyond offering mere predictive models into building interpretable models that appeal to human logic and intuition because they ensure transparency and simplicity and can be used to make meaningful decisions in real-world applications. A second trend in AI is characterized by important advancements in the realm of causal reasoning. Identifying causal relationships is an important aspect of scientific endeavors in a variety of fields. Causal models and Bayesian inference can help us gain better domain-specific insight and make better data-driven decisions because of their interpretability. The main objective of this dissertation was to adapt theoretically sound AI-based interpretable data-analytic approaches to solve domain-specific problems in the two un-related fields of Storage Systems and Public Policy. For the first task, we considered the well-studied problem of cache replacement problem in computing systems, which can be modeled as a variant of the well-known Multi-Armed Bandit (MAB) problem with delayed feedback and decaying costs, and developed an algorithm called EXP4-DFDC. We proved theoretically that EXP4-DFDC exhibits an important feature called vanishing regret. Based on the theoretical analysis, we designed a machine-learning algorithm called ALeCaR, with adaptive hyperparameters. We used extensive experiments on a wide range of workloads to show that ALeCaR performed better than LeCaR, the best machine learning algorithm for cache replacement at that time. We concluded that reinforcement machine learning can offer an outstanding approach for implementing cache management policies. For the second task, we used Bayesian networks to analyze the service request data from three 311 centers providing non-emergency services in the cities of Miami-Dade, New York City, and San Francisco. Using a causal inference approach, this study investigated the presence of inequities in the quality of the 311 services to neighborhoods with varying demographics and socioeconomic status. We concluded that the services provided by the local governments showed no detectable biases on the basis of race, ethnicity, or socioeconomic status