Learned Data Structures, a recent research area overlapping Machine Learning and Data Structure, uses learning from data in order to improve the time/space performance of classic data structures, e.g ., Search Trees, Hash Tables and Bloom Filters. It is also claimed that the use of GPUs for the learning phase contributes to make those approaches widespread. Yet, the role of GPU computing with respect to Learned Data Structures is unclear. Moreover, none of such studies has been devoted to algorithms for Sorted Table Search, that are both fundamental and still widely used in several application domains. We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures  and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter. Specifically, we characterize the scenarios in which those latter can be profitably used with respect to the former, accounting for both CPU and GPU computing. We also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one  and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
Sorted Table Search Procedures are the quintessential query-answering tool, with widespread usage that now includes also Web Applications, e.g , Search Engines (Google Chrome) and ad Bidding Systems (AppNexus). Speeding them up, at very little cost in space, is still a quite significant achievement. Here we study to what extend Machine Learning Techniques can contribute to obtain such a speed-up via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures , with different Data Layouts, and their Learned counterparts developed here . We characterize the scenarios in which those latter can be profitably used with respect to the former, accounting for both CPU and GPU computing. Our approach contributes also to the study of Learned Data Structures, a recent proposal to improve the time/space performance of fundamental Data Structures, e.g., B-trees, Hash Tables, Bloom Filters. Indeed, we also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one proposed here and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
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Learned Data Structures, a recent research area overlapping Machine Learning and Data Structure, uses learning from data in order to improve the time/space performance of classic data structures,
Sorted Table Search Procedures are the quintessential query-answering tool, with widespread usage that now includes also Web Applications,
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., Search Trees, Hash Tables and Bloom Filters. It is also claimed that the use of GPUs for the learning phase contributes to make those approaches widespread. Yet, the role of GPU computing with respect to Learned Data Structures is unclear. Moreover, none of such studies has been devoted to algorithms for Sorted Table Search, that are both fundamental and still widely used in several application domains. We provide such a study here,
, Search Engines (Google Chrome) and ad Bidding Systems (AppNexus). Speeding them up, at very little cost in space, is still a quite significant achievement. Here we study to what extend Machine Learning Techniques can contribute to obtain such a speed-up
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, with different Data Layouts,
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, obtaining valuable methodological insights into the use of those latter. Specifically, we
. We
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We also
Our approach contributes also to the study of Learned Data Structures, a recent proposal to improve the time/space performance of fundamental Data Structures, e.g., B-trees, Hash Tables, Bloom Filters. Indeed, we also
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proposed here