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Ran Rui, Hao Li, Yi-Cheng Tu
Implementing database operations on parallel platforms has gain a lot of momentum in the past decade, due to the increasing popularity of many-core processors. A number of studies have shown the potential of using GPUs to speed up database operations. In this paper, we present empirical evaluations of a state-of-the-art work published in SIGMOD’08 on […]
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Lila Shnaiderman, Oded Shmueli
With an increasing amount of data and demand for fast query processing, the efficiency of database operations continues to be a challenging task. A common approach is to leverage parallel hardware platforms. With the introduction of general-purpose GPU (Graphics Processing Unit) computing, massively parallel hardware has become available within commodity hardware. XML is based on […]
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Martin Krulis, Jakub Yaghob
Actual trend set by CPU manufacturers and recent developement in the field of graphical processing units (GPUs) offered us the computational power of multi-core and many-core architectures. Database applications can benefit greatly from parallelism; however, many algorithms need to be redesigned and many technical issues need to be solved. In this paper, we have focused […]
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Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga Govindaraju, Qiong Luo, Pedro Sander
We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). The most recent GPU features include support for writing to random memory locations, efficient inter-processor communication, and a programming model for general-purpose computing. Taking advantage of these new features, we design a set of data-parallel primitives such as […]
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