SIMD-Conscious Optimization of Star Schema Query Processing

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 차상균.Most modern CPUs today come equipped with SIMD (Single Instruction, Multiple Data) registers and instructions, which allow for data-level parallelism by offering the ability to execute a given instruction on multiple elements of data. With its wide availability and compiler support, lack of need for hardware changes and potential for boosting performance, exploiting SIMD instructions in database query processing has been the subject of some attention in literature. Star schemas are a popular method of data mart modeling, and with the sharp rise in the need for efficient big data analysis, star schemas serve as an important case study for OLAP performance optimization. Whilst literature on SIMD optimization of star schema queries exists for the GPGPU domain - where the GPGPU method of execution is synonymous with the SIMD paradigm - none has explored the topic using SIMD instructions on CPUs. In this paper, we show that by optimizing star schema query processing for SIMD instructions, speedup in excess of four times can be achieved in performance. Instead of relying on the traditional operator-based query processing model, we focus on the so-called invisible joinan algorithm specialized for star schema joins. We describe the steps and procedures involved in the SIMD-conscious optimization of the invisible join algorithm, and demonstrate that our SIMD optimization methods achieve up to 4.8x overall speedup over its scalar equivalent, and up to 6.4x speedup for specific operations.Abstract I Table of Contents III List of Figures V Chapter 1. Introduction 1 Chapter 2. Related Work 5 Chapter 3. Star Schema and Invisible Join 7 2.1 The Star Schema 7 2.2 The Invisible Join 8 Chapter 4. SIMDification of Invisible Join 13 4.1 Extending the Invisible Join 13 4.2 SIMDification of the Invisible Join 15 Chapter 5. Experimental Results 21 5.1 Experimental Setup 21 5.2 Overall Results 22 5.3 Breakdown of Results 23 Chapter 6. Conclusion and Future Work 30 References 32 국문 초록 35 Acknowledgements 37Maste

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