The availability of low cost sensors has led to an unprecedented growth in the volume of spatial data. However, the time required to evaluate even simple spatial queries over large data sets greatly hampers our ability to interactively explore these data sets and extract actionable insights. Graphics Processing Units~(GPUs) are increasingly being used to speedup spatial queries. However, existing GPU-based solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is based on CPU-based approaches, it can be difficult to effectively utilize all the benefits provided by the GPU. As a first step towards making GPU spatial query processing mainstream, we propose a new model that represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects. We demonstrate the expressiveness of the proposed algebra by formulating standard spatial queries as algebraic expressions. We also present a proof-of-concept prototype that supports a subset of the operators and show that it is at least two orders of magnitude faster than a CPU-based implementation. This performance gain is obtained both using a discrete Nvidia mobile GPU and the less powerful integrated GPUs common in commodity laptops.
Subgraph isomorphism is a well-known NP-hard problem that is widely used in many applications, such as social network analysis and query over the knowledge graph. Due to the inherent hardness, its performance is often a bottleneck in various real-world applications. Therefore, we address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture, such as massive parallelism and memory hierarchy. Existing GPU-based solutions adopt a two-step output scheme, performing the same join process twice in order to write intermediate results concurrently. They …
Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main-memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail. Graphics Processing Units (GPUs), which are now widely available even in commodity desktops and laptops, provide a cost-effective alterna…
Efficient $(α,β)$-core Computation and On-the-fly Query at Billion Scale with GPUs
Great Bay University（大湾区大学）；Alibaba Group（阿里巴巴集团）；Shanghai Jiao Tong University（上海交通大学）；University of New South Wales（新南威尔士大学）；Wuhan University of Technology（武汉科技大学）
Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part invoking one or more trained models to perform predictions. These parts have so far been optimized in isolation, leaving significant opportunities for optimization unexplored. We present Raven, a production-ready system for optimizing prediction queries. Raven follows the enterprise architectural trend of collocating data and ML runtimes. It relies o…
FROG: Efficient Range-Filtering Approximate Nearest Neighbor Search on GPUs
Xidian University（西安电子科技大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an University of Posts and Telecommunications（西安邮电大学）
