Parallelizing Set Similarity Joins

Eine der größten Herausforderungen in Data Science ist heutzutage, Daten miteinander in Beziehung zu setzen und ähnliche Daten zu finden. Hierzu kann der aus relationalen Datenbanken bekannte Join-Operator eingesetzt werden. Das Konzept der Ähnlichkeit wird häufig durch mengenbasierte Ähnlichkeitsfunktionen gemessen. Um solche Funktionen als Join-Prädikat nutzen zu können, setzt diese Arbeit voraus, dass Records aus Mengen von Tokens bestehen. Die Arbeit fokussiert sich auf den mengenbasierten Ähnlichkeitsjoin, Set Similarity Join (SSJ). Die Datenmenge, die es heute zu verarbeiten gilt, ist groß und wächst weiter. Der SSJ hingegen ist eine rechenintensive Operation. Um ihn auf großen Daten ausführen zu können, sind neue Ansätze notwendig. Diese Arbeit fokussiert sich auf das Mittel der Parallelisierung. Sie leistet folgende drei Beiträge auf dem Gebiet der SSJs. Erstens beschreibt und untersucht die Arbeit den aktuellen Stand paralleler SSJ-Ansätze. Diese Arbeit vergleicht zehn Map-Reduce-basierte Ansätze aus der Literatur sowohl analytisch als auch experimentell. Der größte Schwachpunkt aller Ansätze ist überraschenderweise eine geringe Skalierbarkeit aufgrund zu hoher Datenreplikation und/ oder ungleich verteilter Daten. Keiner der Ansätze kann den SSJ auf großen Daten berechnen. Zweitens macht die Arbeit die verfügbare hohe CPU-Parallelität moderner Rechner für den SSJ nutzbar. Sie stellt einen neuen daten-parallelen multi-threaded SSJ-Ansatz vor. Der vorgestellte Ansatz ermöglicht erhebliche Laufzeit-Beschleunigungen gegenüber der Ausführung auf einem Thread. Drittens stellt die Arbeit einen neuen hoch skalierbaren verteilten SSJ-Ansatz vor. Mit einer kostenbasierten Heuristik und einem daten-unabhängigen Skalierungsmechanismus vermeidet er Daten-Replikation und wiederholte Berechnungen. Der Ansatz beschleunigt die Join-Ausführung signifikant und ermöglicht die Ausführung auf erheblich größeren Datenmengen als bisher betrachtete parallele Ansätze.One of today's major challenges in data science is to compare and relate data of similar nature. Using the join operation known from relational databases could help solving this problem. Given a collection of records, the join operation finds all pairs of records, which fulfill a user-chosen predicate. Real-world problems could require complex predicates, such as similarity. A common way to measure similarity are set similarity functions. In order to use set similarity functions as predicates, we assume records to be represented by sets of tokens. In this thesis, we focus on the set similarity join (SSJ) operation. The amount of data to be processed today is typically large and grows continually. On the other hand, the SSJ is a compute-intensive operation. To cope with the increasing size of input data, additional means are needed to develop scalable implementations for SSJ. In this thesis, we focus on parallelization. We make the following three major contributions to SSJ. First, we elaborate on the state-of-the-art in parallelizing SSJ. We compare ten MapReduce-based approaches from the literature analytically and experimentally. Their main limit is surprisingly a low scalability due to too high and/or skewed data replication. None of the approaches could compute the join on large datasets. Second, we leverage the abundant CPU parallelism of modern commodity hardware, which has not yet been considered to scale SSJ. We propose a novel data-parallel multi-threaded SSJ. Our approach provides significant speedups compared to single-threaded executions. Third, we propose a novel highly scalable distributed SSJ approach. With a cost-based heuristic and a data-independent scaling mechanism we avoid data replication and recomputation. A heuristic assigns similar shares of compute costs to each node. Our approach significantly scales up the join execution and processes much larger datasets than all parallel approaches designed and implemented so far

A Heterogeneous High Performance Computing Framework For Ill-Structured Spatial Join Processing

The frequently employed spatial join processing over two large layers of polygonal datasets to detect cross-layer polygon pairs (CPP) satisfying a join-predicate faces challenges common to ill-structured sparse problems, namely, that of identifying the few intersecting cross-layer edges out of the quadratic universe. The algorithmic engineering challenge is compounded by GPGPU SIMT architecture. Spatial join involves lightweight filter phase typically using overlap test over minimum bounding rectangles (MBRs) to discard majority of CPPs, followed by refinement phase to rigorously test the join predicate over the edges of the surviving CPPs. In this dissertation, we develop new techniques - algorithms, data structure, i/o, load balancing and system implementation - to accelerate the two-phase spatial-join processing. We present a new filtering technique, called Common MBR Filter (CMF), which changes the overall characteristic of the spatial join algorithms wherein the refinement phase is no longer the computational bottleneck. CMF is designed based on the insight that intersecting cross-layer edges must lie within the rectangular intersection of the MBRs of CPPs, their common MBRs (CMBR). We also address a key limitation of CMF for class of spatial datasets with either large or dense active CMBRs by extended CMF, called CMF-grid, that effectively employs both CMBR and grid techniques by embedding a uniform grid over CMBR of each CPP, but of suitably engineered sizes for different CPPs. To show efficiency of CMF-based filters, extensive mathematical and experimental analysis is provided. Then, two GPU-based spatial join systems are proposed based on two CMF versions including four components: 1) sort-based MBR filter, 2) CMF/CMF-grid, 3) point-in-polygon test, and, 4) edge-intersection test. The systems show two orders of magnitude speedup over the optimized sequential GEOS C++ library. Furthermore, we present a distributed system of heterogeneous compute nodes to exploit GPU-CPU computing in order to scale up the computation. A load balancing model based on Integer Linear Programming (ILP) is formulated for this system. We also provide three heuristic algorithms to approximate the ILP. Finally, we develop MPI-cuda-GIS system based on this heterogeneous computing model by integrating our CUDA-based GPU system into a newly designed distributed framework designed based on Message Passing Interface (MPI). Experimental results show good scalability and performance of MPI-cuda-GIS system

Hardware acceleration of similarity queries using graphic processor units

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 93-103A Graphic Processing Unit (GPU) is primarily designed for real-time rendering. In contrast to a Central Processing Unit (CPU) that have complex instructions and a limited number of pipelines, a GPU has simpler instructions and many execution pipelines to process vector data in a massively parallel fashion. In addition to its regular tasks, GPU instruction set can be used for performing other types of general-purpose computations as well. Several frameworks like Brook+, ATI CAL, OpenCL, and Nvidia Cuda have been proposed to utilize computational power of the GPU in general computing. This has provided interest and opportunities for accelerating different types of applications. This thesis explores ways of taking advantage of the GPU in the field of metric space-based similarity searching. The KVP index structure has a simple organization that lends itself to be easily processed in parallel, in contrast to tree-based structures that requires frequent ”pointer chasing” operations. Several implementations using the general purpose GPU programming frameworks (Brook+, ATI CAL and OpenCL) based on the ATI platform are provided. Experimental results of these implementations show that the GPU versions presented in this work are several times faster than the CPU versions.Genç, AtillaM.S

Efficient And Scalable Evaluation Of Continuous, Spatio-temporal Queries In Mobile Computing Environments

A variety of research exists for the processing of continuous queries in large, mobile environments. Each method tries, in its own way, to address the computational bottleneck of constantly processing so many queries. For this research, we present a two-pronged approach at addressing this problem. Firstly, we introduce an efficient and scalable system for monitoring traditional, continuous queries by leveraging the parallel processing capability of the Graphics Processing Unit. We examine a naive CPU-based solution for continuous range-monitoring queries, and we then extend this system using the GPU. Additionally, with mobile communication devices becoming commodity, location-based services will become ubiquitous. To cope with the very high intensity of location-based queries, we propose a view oriented approach of the location database, thereby reducing computation costs by exploiting computation sharing amongst queries requiring the same view. Our studies show that by exploiting the parallel processing power of the GPU, we are able to significantly scale the number of mobile objects, while maintaining an acceptable level of performance. Our second approach was to view this research problem as one belonging to the domain of data streams. Several works have convincingly argued that the two research fields of spatiotemporal data streams and the management of moving objects can naturally come together. [IlMI10, ChFr03, MoXA04] For example, the output of a GPS receiver, monitoring the position of a mobile object, is viewed as a data stream of location updates. This data stream of location updates, along with those from the plausibly many other mobile objects, is received at a centralized server, which processes the streams upon arrival, effectively updating the answers to the currently active queries in real time. iv For this second approach, we present GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous spatio-temporal queries over spatiotemporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. Finally, in an effort to move beyond the analysis of specific algorithms over the GEDS framework, we take a broader approach in our analysis of GPU computing. What algorithms are appropriate for the GPU? What types of applications can benefit from the parallel and stream processing power of the GPU? And can we identify a class of algorithms that are best suited for GPU computing? To answer these questions, we develop an abstract performance model, detailing the relationship between the CPU and the GPU. From this model, we are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based application

Graph Processing on GPU

Ph.DDOCTOR OF PHILOSOPH

High-Performance Computing Frameworks for Large-Scale Genome Assembly

Genome sequencing technology has witnessed tremendous progress in terms of throughput and cost per base pair, resulting in an explosion in the size of data. Typical de Bruijn graph-based assembly tools demand a lot of processing power and memory and cannot assemble big datasets unless running on a scaled-up server with terabytes of RAMs or scaled-out cluster with several dozens of nodes. In the first part of this work, we present a distributed next-generation sequence (NGS) assembler called Lazer, that achieves both scalability and memory efficiency by using partitioned de Bruijn graphs. By enhancing the memory-to-disk swapping and reducing the network communication in the cluster, we can assemble large sequences such as human genomes (~400 GB) on just two nodes in 14.5 hours, and also scale up to 128 nodes in 23 minutes. We also assemble a synthetic wheat genome with 1.1 TB of raw reads on 8 nodes in 18.5 hours and on 128 nodes in 1.25 hours. In the second part, we present a new distributed GPU-accelerated NGS assembler called LaSAGNA, which can assemble large-scale sequence datasets using a single GPU by building string graphs from approximate all-pair overlaps in quasi-linear time. To use the limited memory on GPUs efficiently, LaSAGNA uses a two-level semi-streaming approach from disk through host memory to device memory with restricted access patterns on both disk and host memory. Using LaSAGNA, we can assemble the human genome dataset on a single NVIDIA K40 GPU in 17 hours, and in a little over 5 hours on an 8-node cluster of NVIDIA K20s. In the third part, we present the first distributed 3rd generation sequence (3GS) assembler which uses a map-reduce computing paradigm and a distributed hash-map, both built on a high-performance networking middleware. Using this assembler, we assembled an Oxford Nanopore human genome dataset (~150 GB) in just over half an hour using 128 nodes whereas existing 3GS assemblers could not assemble it because of memory and/or time limitations

High Performance Information Filtering on Many-core Processors

The increasing amount of information accessible to a user digitally makes search difficult, time consuming and unsatisfactory. This has led to the development of active information filtering (recommendation) systems that learn a user’s preference and filter out the most relevant information using sophisticated machine learning techniques. To be scalable and effective, such systems are currently deployed in cloud infrastructures consisting of general-purpose computers. The emergence of many-core processors as compute nodes in cloud infrastructures necessitates a revisit of the computational model, run-time, memory hierarchy and I/O pipelines to fully exploit available concurrency within these processors. This research proposes algorithms & architectures to enhance the performance of content-based (CB) and collaborative information filtering (CF) on many-core processors. To validate these methods, we use Nvidia’s Tesla, Fermi and Kepler GPUs and Intel’s experimental single chip cloud computer (SCC) as the target platforms. We observe that ~290x speedup and up to 97% energy savings over conventional sequential approaches. Finally, we propose and validate a novel reconfigurable SoC architecture which combines the best features of GPUs & SCC. This has been validated to show ~98K speedup over SCC and ~15K speedup over GPU

Efficient Solutions in Path Planning for Autonomous Mobile Robots

Fecha de lectura de Tesis: 25 julio 2018.Los robots, máquinas que desempeñan un abanico de tareas de lo más variopinto. Desde realizar tareas muy específicas en cadenas de montaje hasta desempeñar la mayoría de labores cotidianas que los seres humanos tenemos que afrontar cada día. Como se puede intuir, para esto se necesitan no solo máquinas, sino máquinas dotadas de cierta inteligencia, que surge de la necesidad de que las máquinas abandonen su estatismo y monotonía para comenzar a enfrentarse a un mundo dinámico y ambiguo: nuestro mundo. El principal desencadenante que ha llevado al ser humano a dotar de inteligencia y movilidad a las máquinas es su afán de dominar y, al mismo tiempo, liberarse de un entorno cada vez más estresante. Hay dos aspectos irrefutables que marcan la versatilidad de una máquina: su inteligencia y su movilidad. Hablando de robótica y movilidad surge el problema de cómo y por dónde debe moverse un robot para alcanzar un determinado objetivo sin comprometer su integridad física. Como el significado de moverse puede ser muy amplio, aquí hablaremos de desplazamiento, en el sentido literal de viajar. Y cuando viajamos a algún lugar siempre nos preguntamos lo siguiente: ¿Por dónde vamos? y ¿Cuál es el la mejor alternativa?. Esta problemática, en robótica, se conoce como el problema del Path Planning. En esta tesis doctoral se aborda, de manera innovadora y altamente paralela, el problema del Path Planing sobre mapas reales extensos en un contexto de tiempo real. Este grado de paralelismo se consigue gracias al uso intensivo de las populares GPU (Unidad Gráfica de Procesamiento) y de los bien conocidos chips multi-core. Pero aquí no solo se aborda el problema del Path Planning desde un punto de vista altamente paralelo sino que, de manera transversal, también se aborda desde un punto de vista inteligente aplicando metaheurísticas