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Parallel cube computation on modern CPUs and GPUs

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Abstract

With the popularity of column-store databases, modern multi-core CPUs, and general-purpose computing on graphics processing units (GPGPUs), there will be radical changes in how processing is done in the online analytical processing (OLAP) and data warehousing fields. Cube computation is a core and time-consuming problem which has been researched extensively. However, most of the algorithms have been proposed without considering the prevalent multi-core architectures and column storage. This paper presents a new parallel cube algorithm that takes advantage of multi-core architectures. We first propose a cache-conscious bottom-up computation (BUC) algorithm called CC-BUC that adopts an integrated bottom-up and breadth-first partitioning order. Each dimension is separately stored and processed. In processing each dimension, breadth-first data scanning and results outputting reduce memory I/O and enhance cache locality. Cache misses are limited in a dimension scope, and translation lookaside buffer (TLB) misses are reduced. Based on CC-BUC, we give a multi-core architecture-based cube algorithm called MC-Cubing. Multiple partitions are processed simultaneously and multiple threads undergo parallel execution inside each partition. MC-Cubing is consistent with multi-core architectures and high parallelism. The layout and associated algorithms take advantage of single instruction, multiple data (SIMD) instructions and thread-level parallelism (TLP). We implement and demonstrate the effectiveness of MC-Cubing on two multi-core architectures: multi-core CPUs and GPUs. Experimental results show that the MC-Cubing algorithm can speed up nearly six times faster than BUC in real datasets.

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Authors and Affiliations

  1. Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, 100872, China

    Guoliang Zhou & Hong Chen

  2. China School of Information, Renmin University of China, Beijing, 100872, China

    Guoliang Zhou & Hong Chen

  3. China Department of Information, Baoding Electric Power Vocation & Technology College, Baoding, Hebei, 071051, China

    Guoliang Zhou

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  1. Guoliang Zhou
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  2. Hong Chen
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Correspondence to Guoliang Zhou.

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Zhou, G., Chen, H. Parallel cube computation on modern CPUs and GPUs. J Supercomput 61, 394–417 (2012). https://doi.org/10.1007/s11227-011-0575-7

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  • DOI: https://doi.org/10.1007/s11227-011-0575-7

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