Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms

Runtime Optimizations for Tree-Based Machine Learning Models

Tree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression trees for learning to rank. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processors. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures. Experiments on synthetic data and on three standard learning-to-rank datasets show that our approach is significantly faster than standard implementations

Multi-Version Search and Cache-Conscious Ranking Optimization

Organizations and companies archive many versions of digital data such as web pages, internal emails and so on. Such data is critical for internal investigation, regulatory compliance, and electronic discovery. It is estimated that electronic discovery market that leverages archival data will reach $9.9 billions globally in 2017. It is not uncommon for many businesses to retain archived collections for 10 to 15 years. How to archive these versioned data is worth to study and we are facing many challenges including 1) traditional index occupies too much space for versioned data, 2) traditional search is too slow on versioned data, and 3) how to guarantee high accuracy when improving efficiency in new architecture.In this dissertation, we take the opportunity of the fast development of information retrieval and tackle the problem by proposing a new multi-version search architecture with cache-conscious ranking optimization framework. Specifically, we will first discuss our new versioned search architecture. Then, we will talk about a cache-conscious online ranking algorithm to improve the online part. Finally, we will describe a framework to select best blocking methods and parameters for our algorithm to achieve best performance.Firstly, we present our new multi-version search architecture. We propose an approach that uses cluster-based retrieval to quickly narrow the search scope guided by version representatives at Phase 1 and develops a hybrid index structure with adaptive runtime data traversal to speed up Phase 2 search. The hybrid scheme exploits the advantages of forward index and inverted index based on the term characteristics to minimize the time in extracting positional and other feature information during runtime search. We compare several indexing and data traversal options with different time and space tradeoffs and describe evaluation results to demonstrate their effectiveness. The experiment results show that the proposed scheme can be up-to about 4x as fast as the previous work on solid state drives while retaining good relevance.Secondly, we talk about our 2D blocking algorithm to optimize the online ranking part of the system. Multi-tree ensemble models have been proven to be effective for document ranking. Using a large number of trees can improve accuracy, but it takes time to calculate ranking scores of matched documents. We investigate data traversal methods for fast score calculation with a large ensemble and propose a 2D blocking scheme for better cache utilization with simpler code structure compared to previous work. The experiments with several benchmarks show significant acceleration in score calculation without loss of ranking accuracy.Lastly, we describe a framework to fast select best blocking methods and parameters for our 2D blocking algorithm with the help of a full cache analysis. 2D blocking method is very helpful to improve online search efficiency. However, different traversal methods and blocking parameter settings can exhibit different cache and cost behavior depending on data and architectural characteristics. It is very time-consuming to conduct exhaustive search for performance comparison and optimum selection. We provide an analytic comparison of cache blocking methods on their data access performance for an approximation and propose a fast guided sampling scheme to select a traversal method and blocking parameters for effective use of memory hierarchy. The evaluation studies with three datasets show that within a reasonable amount of time, the proposed scheme can identify a highly competitive solution that significantly accelerates score calculation.In summary, we have proposed a new multi-version search architecture with cache-conscious ranking optimization for the online search part and a framework to help fast select best blocking methods and parameters with full cache analysis for the 2D blocking method. By proposing this new versioned search system, we can meet challenges from scalability, efficiency and accuracy in multi-version search, and we believe this work would be useful to future researchers in this direction

Efficient Similarity Search with Cache-Conscious Data Traversal

Similarity search is important for many data-intensive applications to identify a set of similar objects. Examples of such applications include near-duplicate detection and clustering, collaborative filtering for similarity-based recommendations, search query suggestion, and data cleaning. Conducting similarity search is a time-consuming process, especially when a massive amount of data is involved, and when all pairs are compared. Previous work has used comparison filtering, inverted indexing, and parallel accumulation of partial intermediate results to expedite its execution. However, shuffling intermediate results can incur significant communication overhead as data scales up.We have developed a fast two-stage partition-based approach for all-pairs similarity search which incorporates static partitioning, optimized load balancing, and cache-conscious data traversal. Static partitioning places dissimilar documents into different groups to eliminate unnecessary comparison between their content. To overcome the challenges introduced by skewed distribution of data partition sizes and irregular dissimilarity relationship in large datasets, we conduct computation load balancing for partitioned similarity search, with competitiveness analysis. These techniques can improve performance by one to two orders of magnitude with less unnecessary I/O and data communication and better load balance. We also discuss how to further accelerate similarity search by incorporating incremental computing and approximation methods such as Locality Sensitive Hashing. Because of data sparsity and irregularity, accessing feature vectors in memory for runtime comparison incurs significant overhead in modern memory hierarchy. We have designed and implemented cache-conscious algorithms to improve runtime efficiency in similarity search. The idea of optimizing data layout and traversal patterns is also applied to the search result ranking problem in runtime with multi-tree ensemble models

Multi-Stage Search Architectures for Streaming Documents

The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

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

Method to rank documents by a computer, using additive ensembles of regression trees and cache optimisation, and search engine using such a method

The present invention concerns a novel method to efficiently score documents (texts, images, audios, videos, and any other information file) by using a machine-learned ranking function modeled by an additive ensemble of regression trees. A main contribution is a new representation of the tree ensemble based on bitvectors, where the tree traversal, aimed to detect the leaves that contribute to the final scoring of a document, is performed through efficient logical bitwise operations. In addition, the traversal is not performed one tree after another, as one would expect, but it is interleaved, feature by feature, over the whole tree ensemble. Tests conducted on publicly available LtR datasets confirm unprecedented speedups (up to 6.5×) over the best state-of-the-art methods

Quality versus efficiency in document scoring with learning-to-rank models

Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of docu- ments and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far. The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking mod- els exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features. We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state- of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded imple- mentations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (λ-MART), an algorithm that induces forests of oblivious regression trees. We investigate how the different training parameters impact on the quality versus effi- ciency trade-off, and provide a thorough comparison of several algorithms in the quality- cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget

Doctor of Philosophy

dissertationComputer programs have complex interactions with their underlying hardware, exhibiting complex behaviors as a result. It is critical to understand these programs, as they serve an importantrole: researchers use them to express new ideas in computer science, while many others derive production value from them. In both cases, program understanding leads to mastery over these functions, adding value to human endeavors. Memory behavior is one of the hallmarks of general program behavior: it represents the critical function of retrieving data for the program to work on; it often reflects the overall actions taken by the program, providing a signature of program behavior; and it is often an important performance bottleneck, as the the memory subsystem is typically much slower than the processor. These reasons justify an investigation into the memory behavior of programs. A memory reference trace is a list of memory transactions performed by a program at runtime, a rich data source capturing the whole of a program's interaction with the memory subsystem, and a clear starting point for investigating program memory behavior. However, such a trace is extremely difficult to interpret by mere inspection, as it consists solely of many, many addresses and operation codes, without any more structure or context. This dissertation proposes to use visualization to construct images and animations of the data within a reference trace, thereby visually transmitting structures and events as encoded in the trace. These visualization approaches are designed with different focuses, meant to expose various aspects of the trace. For instance, the time dimension of the reference traces can be handled either with animation, showing events as they occur, or by laying time out in a spatial dimension, giving a view of the entire history of the trace at once. The approaches also vary in their level of abstraction from the hardware: some are concretely connected to representations of the memory itself, while others are more free-form, using more abstract metaphors to highlight general behaviors and patterns, which in turn characterize the program behavior. Each approach delivers its own set of insights, as demonstrated in this dissertation