Handling Massive N-Gram Datasets Efficiently

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source. Regarding the problem of indexing, we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models, that have emerged as the de-facto choice for language modeling in both academia and industry, thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February 2019, Article No: 2

Signature file access methodologies for text retrieval: a literature review with additional test cases

Signature files are extremely compressed versions of text files which can be used as access or index files to facilitate searching documents for text strings. These access files, or signatures, are generated by storing hashed codes for individual words. Given the possible generation of similar codes in the hashing or storing process, the primary concern in researching signature files is to determine the accuracy of retrieving information. Inaccuracy is always represented by the false signaling of the presence of a text string. Two suggested ways to alter false drop rates are: 1) to determine if either of the two methologies for storing hashed codes, by superimposing them or by concatenating them, is more efficient; and 2) to determine if a particular hashing algorithm has any impact. To assess these issues, the history of suprimposed coding is traced from its development as a tool for compressing information onto punched cards in the 1950s to its incorporation into proposed signature file methodologies in the mid-1980\u27 s. Likewise, the concept of compressing individual words by various algorithms, or by hashing them is traced through the research literature. Following this literature review, benchmark trials are performed using both superimposed and concatenated methodologies while varying hashing algorithms. It is determined that while one combination of hashing algorithm and storage methodology is better, all signature file mehods can be considered viable

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Current trends in processor architectures increasingly include more cores on a single chip and more complex memory hierarchies, and such a trend is likely to continue in the foreseeable future. These processors offer unprecedented opportunities for speeding up demanding computations if the available resources can be effectively utilized. Simultaneously, parallel programming languages such as OpenMP and MPI have been commonly used on clusters of multicore CPUs while newer programming languages such as OpenCL and CUDA have been widely adopted on recent heterogeneous systems and GPUs respectively. The main goal of this dissertation is to develop techniques and methodologies for exploiting these emerging parallel architectures and parallel programming languages to solve large scale irregular applications such as the construction of inverted files. The extraction of inverted files from large collections of documents forms a critical component of all information retrieval systems including web search engines. In this problem, the disk I/O throughput is the major performance bottleneck especially when intermediate results are written onto disks. In addition to the I/O bottleneck, a number of synchronization and consistency issues must be resolved in order to build the dictionary and postings lists efficiently. To address these issues, we introduce a dictionary data structure using a hybrid of trie and B-trees and a high-throughput pipeline strategy that completely avoids the use of disks as temporary storage for intermediate results, while ensuring the consumption of the input data at a high rate. The high-throughput pipelined strategy produces parallel parsed streams that are consumed at the same rate by parallel indexers. The pipelined strategy is implemented on a single multicore CPU as well as on a cluster of such nodes. We were able to achieve a throughput of more than 262MB/s on the ClueWeb09 dataset on a single node. On a cluster of 32 nodes, our experimental results show scalable performance using different metrics, significantly improving on prior published results. On the other hand, we develop a new approach for handling time-evolving documents using additional small temporal indexing structures. The lifetime of the collection is partitioned into multiple time windows, which guarantees a very fast temporal query response time at a small space overhead relative to the non-temporal case. Extensive experimental results indicate that the overhead in both indexing and querying is small in this more complicated case, and the query performance can indeed be improved using finer temporal partitioning of the collection. Finally, we employ GPUs to accelerate the indexing process for building inverted files and to develop a very fast algorithm for the highly irregular list ranking problem. For the indexing problem, the workload is split between CPUs and GPUs in such a way that the strengths of both architectures are exploited. For the list ranking problem involved in the decompression of inverted files, an optimized GPU algorithm is introduced by reducing the problem to a large number of fine grain computations in such a way that the processing cost per element is shown to be close to the best possible

An investigation to study the feasibility of on-line bibliographic information retrieval system using an APP

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis reports an investigation on the feasibility study of a searching mechanism using an APP suitable for an on-line bibliographic retrieval, operation, especially for retrospective searches. From the study of the searching methods used in the conventional systems it is seen that elaborate file- and data- structures are introduced to improve the response time of the system. These consequently lead to software and hardware redundancies. To mask these complexities of the system an expensive computer with higher capabilities and more powerful instruction set is commonly used. Thus the service of the systen becomes cost-ineffective. On the other hand the primitive operations of a searching mechanism, such as, association, domain selection, intersection and unions, are the intrinsic features of an associative parallel processor. Therefore it is important to establish the feasibility of an APP as a cost-effective searching mechanise. In this thesis a searching mechanism using an 'ON-THE-FLY' searching technique has been proposed. The parallel search unit uses a Byte-oriented VRL-APP for efficient character string processing. At the time of undertaking this work the specification for neither the retrieval systems nor the BO-VRL APP's were well established; hence a two-phase investigation was originated. In the Phase I of the work a bottom up approach was adopted to derive a formal and precise specification for the BO-VRL-APP. During the Phase II of the work a top-down approach was opted for the implementation of the searching mechanism. An experimental research vehicle has been developed to establish the feasibility of an APP as a cost-effective searching mechanism. Although rigorous proof of the feasibility has not been obtained, the thesis establishes that the APP is well suited for on-line bibligraphic information retrieval operations where substring searches including boolean selection and threshold weights are efficiently supported

Performance Optimizations of NoSQL Databases in Distributed Systems

Databases store information about a system and provide a mechanism for data to be accessed and manipulated. While advancements in the 1970s provided a relational database model that has persisted to this day, web-scale era mass data needs surfacing in the 1990s and the early 2000s revealed limitations in the scalability of the relational model. As systems grew and transitioned into distributed architectures to support mass data storage and parallel processing, a complete overhaul of distributed computing technologies evolved that fundamentally departed from the relational data model in favor of the NoSQL data model. The course of this research details the scaling problems encountered by relational databases and the NoSQL solutions that made web-scale systems possible

Structural Separation Logic

This thesis presents structural separation logic, a novel program reasoning approach for software that manipulates both standard heaps and structured data such as lists and trees. Structural separation logic builds upon existing work in both separation logic and context logic. It considers data abstractly, much as it is exposed by library interfaces, ignoring implementation details. We provide a programming language that works over structural heaps, which are similar to standard heaps but allow data to be stored in an abstract form. We introduce abstract heaps, which extend structural heaps to enable local reasoning about abstract data. Such data can be split up with structural addresses. Structural addresses allow sub-data (e.g. a sub-tree within a tree) to be abstractly allocated, promoting the sub-data to an abstract heap cell. This cell can be analysed in isolation, then re-joined with the original data. We show how the tight footprints this allows can be refined further with promises, which enable abstract heap cells to retain information about the context from which they were allocated. We prove that our approach is sound with respect to a standard Hoare logic. We study two large examples. Firstly, we present an axiomatic semantics for the Docu- ment Object Model in structural separation logic. We demonstrate how structural separa- tion logic allows abstract reasoning about the DOM tree using tighter footprints than were possible in previous work. Secondly, we give a novel presentation of the POSIX file system library. We identify a subset of the large POSIX standard that focuses on the file system, including commands that manipulate both the file heap and the directory structure. Axioms for this system are given using structural separation logic. As file system resources are typically identified by paths, we use promises to give tight footprints to commands, so that that they do not require all the resource needed to explain paths being used. We demonstrate our reasoning using a software installer example.Open Acces

Simplified vector-thread architectures for flexible and efficient data-parallel accelerators

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 165-170).This thesis explores a new approach to building data-parallel accelerators that is based on simplifying the instruction set, microarchitecture, and programming methodology for a vector-thread architecture. The thesis begins by categorizing regular and irregular data-level parallelism (DLP), before presenting several architectural design patterns for data-parallel accelerators including the multiple-instruction multiple-data (MIMD) pattern, the vector single-instruction multiple-data (vector-SIMD) pattern, the single-instruction multiple-thread (SIMT) pattern, and the vector-thread (VT) pattern. Our recently proposed VT pattern includes many control threads that each manage their own array of microthreads. The control thread uses vector memory instructions to efficiently move data and vector fetch instructions to broadcast scalar instructions to all microthreads. These vector mechanisms are complemented by the ability for each microthread to direct its own control flow. In this thesis, I introduce various techniques for building simplified instances of the VT pattern. I propose unifying the VT control-thread and microthread scalar instruction sets to simplify the microarchitecture and programming methodology. I propose a new single-lane VT microarchitecture based on minimal changes to the vector-SIMD pattern.(cont.) Single-lane cores are simpler to implement than multi-lane cores and can achieve similar energy efficiency. This new microarchitecture uses control processor embedding to mitigate the area overhead of single-lane cores, and uses vector fragments to more efficiently handle both regular and irregular DLP as compared to previous VT architectures. I also propose an explicitly data-parallel VT programming methodology that is based on a slightly modified scalar compiler. This methodology is easier to use than assembly programming, yet simpler to implement than an automatically vectorizing compiler. To evaluate these ideas, we have begun implementing the Maven data-parallel accelerator. This thesis compares a simplified Maven VT core to MIMD, vector-SIMD, and SIMT cores. We have implemented these cores with an ASIC methodology, and I use the resulting gate-level models to evaluate the area, performance, and energy of several compiled microbenchmarks. This work is the first detailed quantitative comparison of the VT pattern to other patterns. My results suggest that future data-parallel accelerators based on simplified VT architectures should be able to combine the energy efficiency of vector-SIMD accelerators with the flexibility of MIMD accelerators.by Christopher Francis Batten.Ph.D

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Storage and aggregation for fast analytics systems

Computing in the last decade has been characterized by the rise of data- intensive scalable computing (DISC) systems. In particular, recent years have wit- nessed a rapid growth in the popularity of fast analytics systems. These systems exemplify a trend where queries that previously involved batch-processing (e.g., run- ning a MapReduce job) on a massive amount of data, are increasingly expected to be answered in near real-time with low latency. This dissertation addresses the problem that existing designs for various components used in the software stack for DISC sys- tems do not meet the requirements demanded by fast analytics applications. In this work, we focus specifically on two components: 1. Key-value storage: Recent work has focused primarily on supporting reads with high throughput and low latency. However, fast analytics applications require that new data entering the system (e.g., new web-pages crawled, currently trend- ing topics) be quickly made available to queries and analysis codes. This means that along with supporting reads efficiently, these systems must also support writes with high throughput, which current systems fail to do. In the first part of this work, we solve this problem by proposing a new key-value storage system – called the WriteBuffer (WB) Tree – that provides up to 30× higher write per- formance and similar read performance compared to current high-performance systems. 2. GroupBy-Aggregate: Fast analytics systems require support for fast, incre- mental aggregation of data for with low-latency access to results. Existing techniques are memory-inefficient and do not support incremental aggregation efficiently when aggregate data overflows to disk. In the second part of this dis- sertation, we propose a new data structure called the Compressed Buffer Tree (CBT) to implement memory-efficient in-memory aggregation. We also show how the WB Tree can be modified to support efficient disk-based aggregation.Ph.D

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author