  Big data analytics applications play a significant role in data centers, and
hence it has become increasingly important to understand their behaviors in
order to further improve the performance of data center computer systems, in
which characterizing representative workloads is a key practical problem. In
this paper, after investigating three most impor- tant application domains in
terms of page views and daily visitors, we chose 11 repre- sentative data
analytics workloads and characterized their micro-architectural behaviors by
using hardware performance counters, so as to understand the impacts and
implications of data analytics workloads on the systems equipped with modern
superscalar out-of-order processors. Our study reveals that big data analytics
applications themselves share many inherent characteristics, which place them
in a different class from traditional workloads and scale-out services. To
further understand the characteristics of big data analytics work- loads we
performed a correlation analysis of CPI (cycles per instruction) with other
micro- architecture level characteristics and an investigation of the big data
software stack impacts on application behaviors. Our correlation analysis
showed that even though big data ana- lytics workloads own notable pipeline
front end stalls, the main factors affecting the CPI performance are long
latency data accesses rather than the front end stalls. Our software stack
investigation found that the typical big data software stack significantly
contributes to the front end stalls and incurs bigger working set. Finally we
gave several recommen- dations for architects, programmers and big data system
designers with the knowledge acquired from this paper.

Distributed, Parallel, and Cluster Computing;Performance