  For emerging datacenter (in short, DC) workloads, such as online Internet
services or offline data analytics, how to evaluate the upper bound performance
and provide apple-to-apple comparisons are fundamental problems. To this end, a
unified computation-centric metric is an essential requirement. As the most
important computation-centric performance metric, FLOPS has guided computing
systems evolutions for many years. However, our observations demonstrate that
the average FLOPS efficiency of the DC workloads is only 0.1%, which implies
that FLOPS is inappropriate for DC computing. To address the above issue, we
propose BOPS (Basic Operations Per Second), which is the average number of BOPs
(Basic OPerations) completed per second. We conduct the analysis on the
characteristics of seventeen typical DC workloads and extract the minimum
representative computation operations set, which is composed of integer and
floating point computation operations of arithmetic, comparing and array
addressing. Then, we propose the formalized BOPS definition and the BOPS based
upper bound performance model. Finally, the BOPS measuring tool is also
implemented. We perform experiments with seventeen DC workloads on three
typical Intel processors platforms. First, BOPS can reflect the performance gap
of different computing systems, the bias between the peak BOPS performance gap
and the average DC workloads' wall clock time gap is no more than 10%. Second,
the Sort workload can achieve 32% BOPS efficiency on the experimental platform.
At last, we present two use cases of BOPS. One is the BOPS based system
evaluation, we illustrate that BOPS can compare performance of workloads from
multiple domains. The other is BOPS based optimizations. We show that under the
guiding of the BOPS based upper bound model, the Sort workload and the Redis
workload achieve 4.4X and 1.2X performance improvements respectively.
