As the amount of data explodes rapidly, more and more corporations are using data centers to make effective de cisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became increasingly important to understand their behaviors in order to further improve the performance of data center computer systems. In this paper, after investigating three most important application domains in terms of page views and daily visitors, we choose eleven representative data analysis workloads and characterize their micro-architectural characteristics by using hardware performance counters, in order to understand the impacts and implications of data analysis workloads on the systems equipped with modern superscalar out-of-order pro cessors. Our study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop (SPEC CPU2006), HPC (HPCC), and service workloads, including traditional server workloads (SPECweb200S) and scale-out service workloads (four among six benchmarks in CloudSuite), and accordingly we give several recommendations for architecture and system optimizations. On the basis of our workload characterization work, we released a benchmark suite named DCBench for typical datacenter workloads, including data analysis and service workloads, with an open-source license on our project home page on http://prof.ict.ac.cnIDCBench. We hope that DCBench is helpful for performing architecture and small-to-medium scale system researches for datacenter computing.
Likewise, some researchers show the data analysis workloads are significantly diverse in terms of both speedup performance and micro-architectural characteristics REF .
Characterizing data analysis workloads in data centers
2013 IEEE International Symposium on Workload Characterization (IISWC)
2013 IEEE International Symposium on Workload Characterization (IISWC)
Computer Science