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ABSTRACT
With the proliferation of database workloads on servers, much recent research on server architecture has focused on database system benchmarks. The TPC benchmarks for the two most common server workloads, OLTP and DSS, have been used extensively in the database community to evaluate the database system functionality and performance. Unfortunately, these benchmarks fall short of being effective in microarchitecture and memory system research due to several key shortcomings. First, setting up the experimental environment and tuning these benchmarks to match the workload behavior of interest involves extremely complex procedures. Second, the benchmarks themselves are complex and preclude accurate correlation of microarchitecture-and memory-level bottlenecks to dominant workload characteristics. Finally, industrial-grade configurations of such benchmarks are too large and preclude their use in detailed but slow microarchitectural simulation studies of future servers. In this paper, we first present an analysis of the dominant behavior in DSS and OLTP workloads, and highlight their key processor and memory performance characteristics. We then introduce a systematic scaling framework to scale down the TPC benchmarks. Finally, we propose the DBmbench, consisting of two substantially scaled-down benchmarks: µTPC-H and µTPC-C that accurately (> 95%) capture the processor and memory performance behavior of DSS and OLTP workloads.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 9
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Ryan Johnson , Stavros Harizopoulos , Nikos Hardavellas , Kivanc Sabirli , Ippokratis Pandis , Anastasia Ailamaki , Naju G. Mancheril , Babak Falsafi, To share or not to share?, Proceedings of the 33rd international conference on Very large data bases, September 23-27, 2007, Vienna, Austria
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Layali Rashid , Wessam M. Hassanein , Moustafa A. Hammad, Exploiting multithreaded architectures to improve the hash join operation, Proceedings of the 9th workshop on MEmory performance: DEaling with Applications, systems and architecture, p.46-53, October 26-26, 2008, Toronto, Canada
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