The POSTGRES next generation database management system
Communications of the ACM
Contrasting characteristics and cache performance of technical and multi-user commercial workloads
ASPLOS VI Proceedings of the sixth international conference on Architectural support for programming languages and operating systems
The SPLASH-2 programs: characterization and methodological considerations
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
The SGI Origin: a ccNUMA highly scalable server
Proceedings of the 24th annual international symposium on Computer architecture
Memory system characterization of commercial workloads
Proceedings of the 25th annual international symposium on Computer architecture
Performance characterization of a Quad Pentium Pro SMP using OLTP workloads
Proceedings of the 25th annual international symposium on Computer architecture
ICS '99 Proceedings of the 13th international conference on Supercomputing
The Memory Performance of DSS Commercial Workloads in Shared-Memory Multiprocessors
HPCA '97 Proceedings of the 3rd IEEE Symposium on High-Performance Computer Architecture
Detailed Characterization of a Quad Pentium Pro Server Running TPC-D
ICCD '99 Proceedings of the 1999 IEEE International Conference on Computer Design
Journal of Parallel and Distributed Computing
Speeding-up multiprocessors running DBMS workloads through coherence protocols
International Journal of High Performance Computing and Networking
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In this paper, we present an in-depth analysis of the memory system performance of the DSS commercial workloads on two state-of-the-art multiprocessors: the SGI Origin 2000 and the HP V-Class. Our results show that a single query process takes almost the same amount of cycles in both machines. However, when multiple query processes run simultaneously on the system, the execution time tends to increase more in SGI Origin 2000 than in HP V-Class due to the more expensive communication overhead in SGI Origin 2000. We also show how the rate at which number of data cache misses, context switches and the overall execution time increases when more query processes run simultaneously.