Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Analysis of Benchmark Characteristics and Benchmark Performance Prediction

Rafael H. Saavedra and Alan Jay Smith

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-92-715
December 1992

http://www.eecs.berkeley.edu/Pubs/TechRpts/1992/CSD-92-715.pdf

Standard benchmarking provides the run times for given programs on given machines, but fails to provide insight as to why those results were obtained (either in terms of machine or program characteristics), and fails to provide run times for that program on some other machine, or some other programs on that machine. We have developed a machine-independent model of program execution to characterize both machine performance and program execution. By merging these machine and program characterizations, we can estimate execution time for arbitrary machine/program combinations. Our technique allows us to identify those operations, either on the machine or in the programs, which dominate the benchmark results. This information helps designers in improving the performance of future machines, and users in tuning their applications to better utilize the performance of existing machines.

Here we apply our methodology to characterize benchmarks and predict their execution times. We present extensive run-time statistics for a large set of benchmarks including the SPEC and Perfect Club suites. We show how these statistics can be used to identify important shortcomings in the programs. In addition, we give execution time estimates for a large sample of programs and machines and compare these against benchmark results. Finally, we develop a metric for program similarity that makes it possible to classify benchmarks with respect to a large set of characteristics.


BibTeX citation:

@techreport{Saavedra:CSD-92-715,
    Author = {Saavedra, Rafael H. and Smith, Alan Jay},
    Title = {Analysis of Benchmark Characteristics and Benchmark Performance Prediction},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1992},
    Month = {Dec},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1992/6260.html},
    Number = {UCB/CSD-92-715},
    Abstract = {Standard benchmarking provides the run times for given programs on given machines, but fails to provide insight as to why those results were obtained (either in terms of machine or program characteristics), and fails to provide run times for that program on some other machine, or some other programs on that machine. We have developed a machine-independent model of program execution to characterize both machine performance and program execution. By merging these machine and program characterizations, we can estimate execution time for arbitrary machine/program combinations. Our technique allows us to identify those operations, either on the machine or in the programs, which dominate the benchmark results. This information helps designers in improving the performance of future machines, and users in tuning their applications to better utilize the performance of existing machines. <p>Here we apply our methodology to characterize benchmarks and predict their execution times. We present extensive run-time statistics for a large set of benchmarks including the SPEC and Perfect Club suites. We show how these statistics can be used to identify important shortcomings in the programs. In addition, we give execution time estimates for a large sample of programs and machines and compare these against benchmark results. Finally, we develop a metric for program similarity that makes it possible to classify benchmarks with respect to a large set of characteristics.}
}

EndNote citation:

%0 Report
%A Saavedra, Rafael H.
%A Smith, Alan Jay
%T Analysis of Benchmark Characteristics and Benchmark Performance Prediction
%I EECS Department, University of California, Berkeley
%D 1992
%@ UCB/CSD-92-715
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1992/6260.html
%F Saavedra:CSD-92-715