Title: Data Motif-based Proxy Benchmarks for Big Data and AI Workloads,
Abstract: For the architecture community, reasonable simulation time is a strong
requirement in addition to performance data accuracy. However, emerging big
data and AI workloads are too huge at binary size level and prohibitively
expensive to run on cycle-accurate simulators. The concept of data motif, which
is identified as a class of units of computation performed on initial or
intermediate data, is the first step towards building proxy benchmark to mimic
the real-world big data and AI workloads. However, there is no practical way to
construct a proxy benchmark based on the data motifs to help simulation-based
research. In this paper, we embark on a study to bridge the gap between data
motif and a practical proxy benchmark. We propose a data motif-based proxy
benchmark generating methodology by means of machine learning method, which
combine data motifs with different weights to mimic the big data and AI
workloads. Furthermore, we implement various data motifs using light-weight
stacks and apply the methodology to five real-world workloads to construct a
suite of proxy benchmarks, considering the data types, patterns, and
distributions. The evaluation results show that our proxy benchmarks shorten
the execution time by 100s times on real systems while maintaining the average
system and micro-architecture performance data accuracy above 90%, even
changing the input data sets or cluster configurations. Moreover, the generated
proxy benchmarks reflect consistent performance trends across different
architectures. To facilitate the community, we will release the proxy
benchmarks on the project homepage this http URL.