264 research outputs found

    DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search

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    Kernel Density Estimation (KDE) is a nonparametric method for estimating the shape of a density function, given a set of samples from the distribution. Recently, locality-sensitive hashing, originally proposed as a tool for nearest neighbor search, has been shown to enable fast KDE data structures. However, these approaches do not take advantage of the many other advances that have been made in algorithms for nearest neighbor algorithms. We present an algorithm called Density Estimation from Approximate Nearest Neighbors (DEANN) where we apply Approximate Nearest Neighbor (ANN) algorithms as a black box subroutine to compute an unbiased KDE. The idea is to find points that have a large contribution to the KDE using ANN, compute their contribution exactly, and approximate the remainder with Random Sampling (RS). We present a theoretical argument that supports the idea that an ANN subroutine can speed up the evaluation. Furthermore, we provide a C++ implementation with a Python interface that can make use of an arbitrary ANN implementation as a subroutine for KDE evaluation. We show empirically that our implementation outperforms state of the art implementations in all high dimensional datasets we considered, and matches the performance of RS in cases where the ANN yield no gains in performance.Comment: 24 pages, 1 figure. Submitted for revie

    Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques

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    Nearest-neighbor query processing is a fundamental operation for many image retrieval applications. Often, images are stored and represented by high-dimensional vectors that are generated by feature-extraction algorithms. Since tree-based index structures are shown to be ineffective for high dimensional processing due to the well-known "Curse of Dimensionality", approximate nearest neighbor techniques are used for faster query processing. Locality Sensitive Hashing (LSH) is a very popular and efficient approximate nearest neighbor technique that is known for its sublinear query processing complexity and theoretical guarantees. Nowadays, with the emergence of technology, several diverse application domains require real-time high-dimensional data storing and processing capacity. Existing LSH techniques are not suitable to handle real-time data and queries. In this paper, we discuss the challenges and drawbacks of existing LSH techniques for processing real-time high-dimensional image data. Additionally, through experimental analysis, we propose improvements for existing state-of-the-art LSH techniques for efficient processing of high-dimensional image data.Comment: Accepted and Presented at the 5th International Conference on Signal and Image Processing (SIGI-2019), Dubai, UA

    GPUs as Storage System Accelerators

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    Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

    Leveraging Emerging Hardware to Improve the Performance of Data Analytics Frameworks

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    Department of Computer Science and EngineeringThe data analytics frameworks have evolved along with the growing amount of data. There have been numerous efforts to improve the performance of the data analytics frameworks in- cluding MapReduce frameworks and NoSQL and NewSQL databases. These frameworks have various target workloads and their own characteristicshowever, there is common ground as a data analytics framework. Emerging hardware such as graphics processing units and persistent memory is expected to open up new opportunities for such commonality. The goal of this dis- sertation is to leverage emerging hardware to improve the performance of the data analytics frameworks. First, we design and implement EclipseMR, a novel MapReduce framework that efficiently leverages an extensive amount of memory space distributed among the machines in a cluster. EclipseMR consists of a decentralized DHT-based file system layer and an in-memory cache layer. The in-memory cache layer is designed to store both local and remote data while balancing the load between the servers with proposed Locality-Aware Fair (LAF) job scheduler. The design of EclipseMR is easily extensible with emerging hardwareit can adopt persistent memory as a primary storage layer or cache layer, or it can adopt GPU to improve the performance of map and reduce functions. Our evaluation shows that EclipseMR outperforms Hadoop and Spark for various applications. Second, we propose B 3 -tree and Cache-Conscious Extendible Hashing (CCEH) for the persis- tent memory. The fundamental challenge to design a data structure for the persistent memory is to guarantee consistent transition with 8-bytes of fine-grained atomic write with minimum cost. B 3 -tree is a fully persistent hybrid indexing structure of binary tree and B+-tree that benefits from the strength of both in-memory index and block-based index, and CCEH is a variant of extendible hashing that introduces an intermediate layer between directory and buckets to fully benefit from a cache-sized bucket while minimizing the size of the directory. Both of the data structures show better performance than the corresponding state-of-the-art techniques. Third, we develop a data parallel tree traversal algorithm, Parallel Scan and Backtrack (PSB), for k-nearest neighbor search problem on the GPU. Several studies have been proposed to improve the performance of the query by leveraging GPU as an acceleratorhowever, most of the works focus on the brute-force algorithms. In this work, we overcome the challenges of traversing multi-dimensional hierarchical indexing structure on the GPU such as tiny shared memory and runtime stack, irregular memory access pattern, and warp divergence problem. Our evaluation shows that our data parallel PSB algorithm outperforms both the brute-force algorithm and the traditional branch and bound algorithm.clos
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