GPUs have long been discussed as accelerators for database query processing because of
their high processing power and memory bandwidth. However, two main challenges limit the …

We engineer a GPU implementation of a B-Tree that supports concurrent queries (point,
range, and successor) and updates (insertions and deletions). Our B-tree outperforms the …

Point-in-Polygon (PIP) test is fundamental to spatial databases and GIS. Motivated by the
slow response times in joining large-scale point locations with polygons using traditional …

IoT produces a huge amount of big data as it comprises billions of devices that are
interconnected with each other through internet. Today's majority of the big data part is about …

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and
deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA …

The self-join finds all objects in a dataset within a threshold of each other defined by a
similarity metric. As such, the self-join is a fundamental building block for the field of …

Recently, various research efforts have been conducted to develop strategies for
accelerating multi-dimensional query processing using the Graphics Processing Units …

Proliferation of IoT devices produces the enormous amount of data that need to be stored on
clouds. A main focus of this paper is to ensure the secrecy of data, while it is in transit …

目的大数据时代, 需要对海量空间数据更快速地建立高效索引, 使用递归排序网格(STR)
方法构建的R 树具有优秀的查询性能, 但构建效率不高. 本文利用基于计算机图形处理器(GPU) …

綜述了圖形處理器上的通用計算(GPGPU) 技術以及利用圖形處理器(GPU)
進行數據庫處理的工作. 將GPU 技術的發展劃分為固定功能架構, 分離渲染架構和統一渲染架構 …