Fast and approximate stream mining of quantiles and frequencies using graphics processors

Govindaraju, Naga K.; Raghuvanshi, Nikunj; Manocha, Dinesh

doi:10.1145/1066157.1066227

Cited by 98 publications

(61 citation statements)

References 47 publications

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“…Greß et al [9] introduced an approach based on the adaptive bitonic sorting technique presented by Bilardi et al [10]. Govindaraju et al [11] implemented a sorting solution based on the periodic balanced sorting network method by Dowd et al [12] and one based on bitonic sort [13]. They later presented a hybrid bitonic-radix sort that used both the CPU and the GPU to be able to sort vast quantities of data [14].…”

Section: Introductionmentioning

confidence: 99%

A Practical Quicksort Algorithm for Graphics Processors

Cederman

Tsigas

2008

Algorithms - ESA 2008

121

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Abstract. In this paper we present GPU-Quicksort, an efficient Quicksort algorithm suitable for highly parallel multi-core graphics processors. Quicksort has previously been considered as an inefficient sorting solution for graphics processors, but we show that GPU-Quicksort often performs better than the fastest known sorting implementations for graphics processors, such as radix and bitonic sort. Quicksort can thus be seen as a viable alternative for sorting large quantities of data on graphics processors.

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Section: Introductionmentioning

confidence: 99%

A Practical Quicksort Algorithm for Graphics Processors

Cederman

Tsigas

2008

Algorithms - ESA 2008

121

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“…In the last few years, there has been growing interest in different non-conventional parallel processing architectures such as Graphics processors [11,14], cell broadband engine [10,15], etc. These processors were originally designed for different application domains, but recent results have shown promise in the use of these processors for data management operations.…”

Section: Discussionmentioning

confidence: 99%

Thread cooperation in multicore architectures for frequency counting over multiple data streams

et al. 2009

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Many real-world data stream analysis applications such as network monitoring, click stream analysis, and others require combining multiple streams of data arriving from multiple sources. This is referred to as multi-stream analysis. To deal with high stream arrival rates, it is desirable that such systems be capable of supporting very high processing throughput. The advent of multicore processors and powerful servers driven by these processors calls for efficient parallel designs that can effectively utilize the parallelism of the multicores, since performance improvement is possible only through effective parallelism. In this paper, we address the problem of parallelizing multi-stream analysis in the context of multicore processors. Specifically, we concentrate on parallelizing frequent elements, top-k, and frequency counting over multiple streams. We discuss the challenges in designing an efficient parallel system for multi-stream processing. Our evaluation and analysis reveals that traditional "contention" based locking results in excessive overhead and wait, which in turn leads to severe performance degradation in modern multicore architectures. Based on our analysis, we propose a "cooperation" based locking paradigm for efficient parallelization of frequency counting. The proposed "cooperation" based paradigm removes waits associated with synchronization, and allows replacing locks by much cheaper atomic synchronization primitives. Our implementation of the proposed paradigm to parallelize a well known frequency counting algorithm shows the benefits of the proposed "cooperation" based locking paradigm when compared to the traditional "contention" based locking paradigm. In our experiments, the proposed "cooperation" based design outperforms the traditional "contention" based design by a factor of 2 − 5.5X for synthetic zipfian data sets.

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“…Examples for GPGPU computing are data processing [3], evolutionary algorithms [4], and secret key cryptography [5].…”

Section: General Purpose Gpu Computationsmentioning

confidence: 99%

GPU-Accelerated Montgomery Exponentiation

Fleissner

2007

Computational Science – ICCS 2007

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Abstract. The computing power and programmability of graphics processing units (GPUs) has been successfully exploited for calculations unrelated to graphics, such as data processing, numerical algorithms, and secret key cryptography. In this paper, a new variant of the Montgomery exponentiation algorithm that exploits the processing power and parallelism of GPUs is designed and implemented. Furthermore, performance tests are conducted and the suitability of the proposed algorithm for accelerating public key encryption is discussed.

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Fast and approximate stream mining of quantiles and frequencies using graphics processors

Cited by 98 publications

References 47 publications

A Practical Quicksort Algorithm for Graphics Processors

A Practical Quicksort Algorithm for Graphics Processors

Thread cooperation in multicore architectures for frequency counting over multiple data streams

GPU-Accelerated Montgomery Exponentiation

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