GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

The heterogeneous computing paradigm has led to the need for portable and efficient programming solutions that can leverage the capabilities of various hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the portability and performance of the SYCL and CUDA languages for one fundamental bioinformatics application (Smith-Waterman protein database search) across different GPU architectures, considering single and multi-GPU configurations from different vendors. The experimental work showed that, while both CUDA and SYCL versions achieve similar performance on NVIDIA devices, the latter demonstrated remarkable code portability to other GPU architectures, such as AMD and Intel. Furthermore, the architectural efficiency rates achieved on these devices were superior in 3 of the 4 cases tested. This brief study highlights the potential of SYCL as a viable solution for achieving both performance and portability in the heterogeneous computing ecosystem.Comment: This article was accepted for publication in 2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD

High Performance Computing for DNA Sequence Alignment and Assembly

Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical

Locality Enhancement and Dynamic Optimizations on Multi-Core and GPU

Enhancing the match between software executions and hardware features is key to computing efficiency. The match is a continuously evolving and challenging problem. This dissertation focuses on the development of programming system support for exploiting two key features of modern hardware development: the massive parallelism of emerging computational accelerators such as Graphic Processing Units (GPU), and the non-uniformity of cache sharing in modern multicore processors. They are respectively driven by the important role of accelerators in today\u27s general-purpose computing and the ultimate importance of memory performance. This dissertation particularly concentrates on optimizing control flows and memory references, at both compilation and execution time, to tap into the full potential of pure software solutions in taking advantage of the two key hardware features.;Conditional branches cause divergences in program control flows, which may result in serious performance degradation on massively data-parallel GPU architectures with Single Instruction Multiple Data (SIMD) parallelism. On such an architecture, control divergence may force computing units to stay idle for a substantial time, throttling system throughput by orders of magnitude. This dissertation provides an extensive exploration of the solution to this problem and presents program level transformations based upon two fundamental techniques --- thread relocation and data relocation. These two optimizations provide fundamental support for swapping jobs among threads so that the control flow paths of threads converge within every SIMD thread group.;In memory performance, this dissertation concentrates on two aspects: the influence of nonuniform sharing on multithreading applications, and the optimization of irregular memory references on GPUs. In shared cache multicore chips, interactions among threads are complicated due to the interplay of cache contention and synergistic prefetching. This dissertation presents the first systematic study on the influence of non-uniform shared cache on contemporary parallel programs, reveals the mismatch between the software development and underlying cache sharing hierarchies, and further demonstrates it by proposing and applying cache-sharing-aware data transformations that bring significant performance improvement. For the second aspect, the efficiency of GPU accelerators is sensitive to irregular memory references, which refer to the memory references whose access patterns remain unknown until execution time (e.g., A[P[i]]). The root causes of the irregular memory reference problem are similar to that of the control flow problem, while in a more general and complex form. I developed a framework, named G-Streamline, as a unified software solution to dynamic irregularities in GPU computing. It treats both types of irregularities at the same time in a holistic fashion, maximizing the whole-program performance by resolving conflicts among optimizations

Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes pose a significant challenge due to the substantial increase in main memory requirements. A potential solution involves leveraging disk-based implementation, which stores and searches vector data on high-performance devices like NVMe SSDs. However, implementing HVSS for data segments proves to be intricate in vector databases where a single machine comprises multiple segments for system scalability. In this context, each segment operates with limited memory and disk space, necessitating a delicate balance between accuracy, efficiency, and space cost. Existing disk-based methods fall short as they do not holistically address all these requirements simultaneously. In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment. It has two primary components: (1) a data layout incorporating an in-memory navigation graph and a reordered disk-based graph with enhanced locality, reducing the search path length and minimizing disk bandwidth wastage; and (2) a block search strategy designed to minimize costly disk I/O operations during vector query execution. Through extensive experiments, we validate the effectiveness, efficiency, and scalability of Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with over 0.9 average precision and top-10 recall rate, and latency under 1 millisecond. The results showcase Starling's superior performance, exhibiting 43.9$\times$ higher throughput with 98% lower query latency compared to state-of-the-art methods while maintaining the same level of accuracy.Comment: This paper has been accepted by SIGMOD 202

Index compression for information retrielval systems

[Abstract] Given the increasing amount of information that is available today, there is a clear need for Information Retrieval (IR) systems that can process this information in an efficient and effective way. Efficient processing means minimising the amount of time and space required to process data, whereas effective processing means identifying accurately which information is relevant to the user and which is not. Traditionally, efficiency and effectiveness are at opposite ends (what is beneficial to efficiency is usually harmful to effectiveness, and vice versa), so the challenge of IR systems is to find a compromise between efficient and effective data processing. This thesis investigates the efficiency of IR systems. It suggests several novel strategies that can render IR systems more efficient by reducing the index size of IR systems, referred to as index compression. The index is the data structure that stores the information handled in the retrieval process. Two different approaches are proposed for index compression, namely document reordering and static index pruning. Both of these approaches exploit document collection characteristics in order to reduce the size of indexes, either by reassigning the document identifiers in the collection in the index, or by selectively discarding information that is less relevant to the retrieval process by pruning the index. The index compression strategies proposed in this thesis can be grouped into two categories: (i) Strategies which extend state of the art in the field of efficiency methods in novel ways. (ii) Strategies which are derived from properties pertaining to the effectiveness of IR systems; these are novel strategies, because they are derived from effectiveness as opposed to efficiency principles, and also because they show that efficiency and effectiveness can be successfully combined for retrieval. The main contributions of this work are in indicating principled extensions of state of the art in index compression, and also in suggesting novel theoretically-driven index compression techniques which are derived from principles of IR effectiveness. All these techniques are evaluated extensively, in thorough experiments involving established datasets and baselines, which allow for a straight-forward comparison with state of the art. Moreover, the optimality of the proposed approaches is addressed from a theoretical perspective.[Resumen] Dada la creciente cantidad de información disponible hoy en día, existe una clara necesidad de sistemas de Recuperación de Información (RI) que sean capaces de procesar esa información de una manera efectiva y eficiente. En este contexto, eficiente significa cantidad de tiempo y espacio requeridos para procesar datos, mientras que efectivo significa identificar de una manera precisa qué información es relevante para el usuario y cual no lo es. Tradicionalmente, eficiencia y efectividad se encuentran en polos opuestos - lo que es beneficioso para la eficiencia, normalmente perjudica la efectividad y viceversa - así que un reto para los sistemas de RI es encontrar un compromiso adecuado entre el procesamiento efectivo y eficiente de los datos. Esta tesis investiga el problema de la eficiencia de los sistemas de RI. Sugiere diferentes estrategias novedosas que pueden permitir la reducción de los índices de los sistemas de RI, enmarcadas dentro da las técnicas conocidas como compresión de índices. El índice es la estructura de datos que almacena la información utilizada en el proceso de recuperación. Se presentan dos aproximaciones diferentes para la compresión de los índices, referidas como reordenación de documentos y pruneado estático del índice. Ambas aproximaciones explotan características de colecciones de documentos para reducir el tamaño final de los índices, mediante la reasignación de los identificadores de los documentos de la colección o bien descartando selectivamente la información que es "menos relevante" para el proceso de recuperación. Las estrategias de compresión propuestas en este tesis se pueden agrupar en dos categorías: (i) estrategias que extienden el estado del arte en la eficiencia de una manera novedosa y (ii) estrategias derivadas de propiedades relacionadas con los principios de la efectividad en los sistemas de RI; estas estrategias son novedosas porque son derivadas desde principios de la efectividad como contraposición a los de la eficiencia, e porque revelan como la eficiencia y la efectividad pueden ser combinadas de una manera efectiva para la recuperación de información. Las contribuciones de esta tesis abarcan la elaboración de técnicas del estado del arte en compresión de índices y también en la derivación de técnicas de compresión basadas en fundamentos teóricos derivados de los principios de la efectividad de los sistemas de RI. Todas estas técnicas han sido evaluadas extensamente con numerosos experimentos que involucran conjuntos de datos y técnicas de referencia bien establecidas en el campo, las cuales permiten una comparación directa con el estado del arte. Finalmente, la optimalidad de las aproximaciones presentadas es tratada desde una perspectiva teórica

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data

The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme --Abstract, pages iii-iv