Parallel Traversal of Large Ensembles of Decision Tree

Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. The deployment of such models is computationally demanding: to compute the final prediction, the whole ensemble must be traversed by accumulating the contributions of all its trees. In particular, traversal cost impacts applications where the number of candidate items is large, the time budget available to apply the learnt model to them is limited, and the users' expectations in terms of quality-of-service is high. Document ranking in web search, where sub-optimal ranking models are deployed to find a proper trade-off between efficiency and effectiveness of query answering, is probably the most typical example of this challenging issue. This paper investigates multi/many-core parallelization strategies for speeding up the traversal of large ensembles of regression trees thus obtaining machine-learnt models that are, at the same time, effective, fast, and scalable. Our best results are obtained by the GPU-based parallelization of the state-of-the-art algorithm, with speedups of up to 102.6x. IEE

Efficient query processing for scalable web search

Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

GPU-based parallelization of QuickScorer to speed-up document ranking with tree ensembles

Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees currently represents one of the most effective solutions to rank query results returned by large scale Information Retrieval systems. However, such scoring models are very complex, and when deployed in real Web Search Engine infrastructures they are constrained within strict time budgets. This calls for very fast and efficient solutions, able to exploit all the computational resources offered by a given system. This paper investigates the opportunities offered by modern graphic cards (GPUs) to efficiently exploit LtR complex models based on trees ensembles to rank documents. To this end we propose GPUScorer, a GPU-based parallelization of the state-of-the-art algorithm QuickScorer to score documents with tree ensembles. GPUScorer takes advantage of the huge computational power of GPUs to perform tree ensemble traversal by evaluating multiple documents simultaneously. We provide a concise experimental evaluation, and show that GPUScorer is able to achieve speedups up to 32x over the sequential version of QuickScorer

Efficient Design, Training, and Deployment of Artificial Neural Networks

Over the last decade, artificial neural networks, especially deep neural networks, have emerged as the main modeling tool in Machine Learning, allowing us to tackle an increasing number of real-world problems in various fields, most notably, in computer vision, natural language processing, biomedical and financial analysis. The success of deep neural networks can be attributed to many factors, namely the increasing amount of data available, the developments of dedicated hardware, the advancements in optimization techniques, and especially the invention of novel neural network architectures. Nowadays, state-of-the-arts neural networks that achieve the best performance in any field are usually formed by several layers, comprising millions, or even billions of parameters. Despite spectacular performances, optimizing a single state-of- the-arts neural network often requires a tremendous amount of computation, which can take several days using high-end hardware. More importantly, it took several years of experimentation for the community to gradually discover effective neural network architectures, moving from AlexNet, VGGNet, to ResNet, and then DenseNet. In addition to the expensive and time-consuming experimentation process, deep neural networks, which require powerful processors to operate during the deployment phase, cannot be easily deployed to mobile or embedded devices. For these reasons, improving the design, training, and deployment of deep neural networks has become an important area of research in the Machine Learning field. This thesis makes several contributions in the aforementioned research area, which can be grouped into two main categories. The first category consists of research works that focus on designing efficient neural network architectures not only in terms of accuracy but also computational complexity. In the first contribution under this category, the computational efficiency is first addressed at the filter level through the incorporation of a handcrafted design for convolutional neural networks, which are the basis of most deep neural networks. More specifically, the multilinear convolution filter is proposed to replace the linear convolution filter, which is a fundamental element in a convolutional neural network. The new filter design not only better captures multidimensional structures inherent in CNNs but also requires far fewer parameters to be estimated. While using efficient algebraic transforms and approximation techniques to tackle the design problem can significantly reduce the memory and computational footprint of neural network models, this approach requires a lot of trial and error. In addition, the simple neuron model used in most neural networks nowadays, which only performs a linear transformation followed by a nonlinear activation, cannot effectively mimic the diverse activities of biological neurons. For this reason, the second and third contributions transition from a handcrafted, manual design approach to an algorithmic approach in which the type of transformations performed by each neuron as well as the topology of neural networks are optimized in a systematic and completely data-dependent manner. As a result, the algorithms proposed in the second and third contributions are capable of designing highly accurate and compact neural networks while requiring minimal human efforts or intervention in the design process. Despite significant progress has been made to reduce the runtime complexity of neural network models on embedded devices, the majority of them have been demonstrated on powerful embedded devices, which are costly in applications that require large-scale deployment such as surveillance systems. In these scenarios, complete on-device processing solutions can be infeasible. On the contrary, hybrid solutions, where some preprocessing steps are conducted on the client side while the heavy computation takes place on the server side, are more practical. The second category of contributions made in this thesis focuses on efficient learning methodologies for hybrid solutions that take into ac- count both the signal acquisition and inference steps. More concretely, the first contribution under this category is the formulation of the Multilinear Compressive Learning framework in which multidimensional signals are compressively acquired, and inference is made based on the compressed signals, bypassing the signal reconstruction step. In the second contribution, the relationships be- tween the input signal resolution, the compression rate, and the learning performance of Multilinear Compressive Learning systems are empirically analyzed systematically, leading to the discovery of a surrogate performance indicator that can be used to approximately rank the learning performances of different sensor configurations without conducting the entire optimization process. Nowadays, many communication protocols provide support for adaptive data transmission to maximize the data throughput and minimize energy consumption depending on the network’s strength. The last contribution of this thesis proposes an extension of the Multilinear Compressive Learning framework with an adaptive compression capability, which enables us to take advantage of the adaptive rate transmission feature in existing communication protocols to maximize the informational content throughput of the whole system. Finally, all methodological contributions of this thesis are accompanied by extensive empirical analyses demonstrating their performance and computational advantages over existing methods in different computer vision applications such as object recognition, face verification, human activity classification, and visual information retrieval