Abstract
Relational databases remain an important application infrastructure for organizing and analyzing massive volumes of data. At the same time, processor architectures are increasingly gravitating towards Multi-Bulk-Synchronous processor (Multi-BSP) architectures employing throughput-optimized memory systems, lightweight multi-threading, and Single-Instruction Multiple-Data (SIMD) core organizations. This paper explores the mapping of primitive relational algebra operations onto such architectures to improve the throughput of data warehousing applications built on relational databases.
- H. Wu, G. Diamos, S. Cadambi, and S. Yalamanchili. Kernel weaver: Automatically fusing database primitives for efficient gpu computation. In Proceedings of the 45th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-45 '12, pages 107--118, 2012. Google Scholar
Digital Library
- G. Diamos, H. Wu, A. Lele, J. Wang, and S. Yalamanchili. Efficient relational algebra algorithms and data structures for gpu. Technical Report GIT-CERCS-12-01, CERCS, Georgia Institute of Technology, 2012.Google Scholar
Index Terms
Relational algorithms for multi-bulk-synchronous processors
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