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
@cite_51: The large input datasets in the SPEC 2000 benchmark suite result in unreasonably long simulation times when using detailed execution-driven simulators for evaluating future computer architectures ideas. To address this problem, we have an ongoing project to reduce the execution times of the SPEC 2000 benchmarks in a quantitatively defensible way. Upon completion of this work 1, we will have smaller input datasets for several SPEC2000 benchmarks. The programs using our reduced input datasets will produce execution profiles that accurately reflect the program behavior of the full reference dataset, as measured using standard statistical tests. In the process of reducing and verifying the SPEC2000 Benchmark datasets, we also obtain instuction mix, memory behavior, and instructions per cycle characterization information about each benchmark program.
@cite_49: Computer architects must determine how tomost effectively use finite computational resources whenrunning simulations to evaluate new architectural ideas.To facilitate efficient simulations with a range of benchmarkprograms, rn have developed the MinneSPEC inputset for the SPEC CPU 2000 benchmark suite. Thisnew workload allows computer architects to obtain simulationresults in a reasonable time using existing sirnulators.While the MinneSPEC workload is derived from thestandard SPEC CPU 2000 warklcad, it is a valid benchmarksuite in and of itself for simulation-based research.MinneSPEC also may be used to run Iarge numbers ofsimulations to find "sweet spots" in the evaluation parameterspace. This small number of promising designpoints subsequently may be investigated in more detailwith the full SPEC reference workload. In the processof developing the MinneSPEC datasets, we quantify itsdifferences in terms of function-level execution patterns,instruction mixes, and memory behaviors compared tothe SPEC programs when executed with the reference inputs.We find that for some programs, the MinneSPECprofiles match the SPEC reference dataset program behaviorvery closely. For other programs, however, theMinneSPEC inputs produce significantly different programbehavior. The MinneSPEC workload has been recognizedby SPEC and is distributed with Version 1.2 andhigher of the SPEC CPU 2000 benchmark suite.
Using reduced data input is one way to reduce execution time. Previous work @cite_51 @cite_49 adopts reduced data set for the SPEC benchmark and maintains similar architecture behaviors with that of using the full reference data sets. However, it does not suit for big data and AI workloads, since they are data centric computing.