A Partition-Merge Based Cache-Conscious Parallel Sorting Algorithm for CMP with Shared Cache

Song, Hao; Du, Z.; Bader, David A.; Yin, Yunfei

doi:10.1109/icpp.2009.26

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…However, such an approach would be both tedious to program and immensely inefficient, given the obvious inter-relationships between the various views. Consequently, in 1995, the data cube operator (an SQL syntactical extension) was proposed by Gray et al [9] as a means of simplifying the process of data cube construction. Subsequent to the publication of the seminal data cube paper, a number of independent research projects began to focus on designing efficient algorithms for the computation of the data cube [4,5,10,11,12,15,16,17,19,21,22,23,24].…”

Section: Background (Review)mentioning

confidence: 99%

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Parallel Construction of Data Cubes on Multi-Core Multi-Disk Platforms

Dehne

Zaboli

2013

Parallel Process. Lett.

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On-line Analytical Processing (OLAP) has become one of the most powerful and prominent technologies for knowledge discovery in VLDB (Very Large Database) environments. Central to the OLAP paradigm is the data cube, a multi dimensional hierarchy of aggregate values that provides a rich analytical model for decision support. Various sequential algorithms for the efficient generation of the data cube have appeared in the literature. However, given the size of contemporary data warehousing repositories, multi-processor solutions are crucial for the massive computational demands of current and future OLAP systems. In this paper we discuss the development of MCMD-CUBE, a new parallel data cube construction method for multi-core processors with multiple disks. We present experimental results for a Sandy Bridge multi-core processor with four parallel disks. Our experiments indicate that MCMD-CUBE achieves very close to linear speedup. A critical part of our MCMD-CUBE method is parallel sorting. We developed a new parallel sorting method termed MCMD-SORT for multi-core processors with multiple disks which outperforms other previous methods.

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Section: Background (Review)mentioning

confidence: 99%

“…Recently, many sorting methods have been proposed for multi-core processors. STL sort available in the STL library, libmt sort [13], [9], [18] are state-of-the-art sorting methods. We tested these sorting methods on in memory data to choose the fastest of them.…”

Section: Mcmd-sort Algorithmmentioning

confidence: 99%

Parallel Construction of Data Cubes on Multi-Core Multi-Disk Platforms

Dehne

Zaboli

2013

Parallel Process. Lett.

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“…Recently, many sorting methods have been proposed for multi-core processors. STL sort available in the standard STL library and libmt sort [82], [62], [97] are state-of-the-art sorting methods for multi-core processors. We tested these sorting methods on in memory data to choose the fastest of them.…”

Section: Mcmd-sort Methodsmentioning

confidence: 99%

Parallel Olap on Multi/Many-Core and Cloud Platforms

Zaboli¹

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One of the most powerful and prominent technologies for knowledge discovery in Decision Support System environments is Online Analytical Processing (OLAP). OLAP is the foundation for a wide range of essential business applications. Since its introduction, OLAP has consistently required a massive computational power. On the other hand, the physical limits of the speed for single processor systems bound the performance of any single processor solution. Parallel and distributed processing can provide two key ingredients to solve First and above all, I am grateful to my supervisor, Dr. Frank Dehne who always had a welcoming attitude, and provided me with a great support in every step of my Ph.D.course. This thesis would not have been possible without his guidance and supervision.

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On Parallel Sorting of Data Streams

Bulánek

Yaghob

2013

Advances in Intelligent Systems and Computing

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A Partition-Merge Based Cache-Conscious Parallel Sorting Algorithm for CMP with Shared Cache

Cited by 6 publications

References 10 publications

Parallel Construction of Data Cubes on Multi-Core Multi-Disk Platforms

Parallel Construction of Data Cubes on Multi-Core Multi-Disk Platforms

Parallel Olap on Multi/Many-Core and Cloud Platforms

On Parallel Sorting of Data Streams

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