Proteus: A Self-Designing Range Filter

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.

Learning Key-Value Store Design

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs. The core intuition behind the construction of design continuums is that all data structures arise from the very same set of fundamental design principles, i.e., a small set of data layout design concepts out of which we can synthesize any design that exists in the literature as well as new ones. We show how to construct, evaluate, and expand, design continuums and we also present the first continuum that unifies major data structure designs, i.e., B+tree, B-epsilon-tree, LSM-tree, and LSH-table. The practical benefit of a design continuum is that it creates a fast inference engine for the design of data structures. For example, we can predict near instantly how a specific design change in the underlying storage of a data system would affect performance, or reversely what would be the optimal data structure (from a given set of designs) given workload characteristics and a memory budget. In turn, these properties allow us to envision a new class of self-designing key-value stores with a substantially improved ability to adapt to workload and hardware changes by transitioning between drastically different data structure designs to assume a diverse set of performance properties at will.

Rapid Training of Very Large Ensembles of Diverse Neural Networks

Ensembles of deep neural networks with diverse architectures significantly improve generalization accuracy. However, training such ensembles requires a large amount of computational resources and time as every network in the ensemble has to be separately trained. In practice, this restricts the number of different deep neural network architectures that can be included within an ensemble. We propose a new approach to address this problem. Our approach captures the structural similarity between members of a neural network ensemble and train it only once. Subsequently, this knowledge is transferred to all members of the ensemble using function-preserving transformations. Then, these ensemble networks converge significantly faster as compared to training from scratch. We show through experiments on CIFAR-10, CIFAR-100, and SVHN data sets that our approach can train large and diverse ensembles of deep neural networks achieving comparable accuracy to existing approaches in a fraction of their training time. In particular, our approach trains an ensemble of $100$ variants of deep neural networks with diverse architectures up to $6 \times$ faster as compared to existing approaches. This improvement in training cost grows linearly with the size of the ensemble.