J. N. Amaral, E. Borin, Dylan R. Ashley, C. Benedicto, Elliot Colp, Joao Henrique Stange Hoffmam, Marcus Karpoff, Erick Ochoa, Morgan Redshaw, R. E. Rodrigues
{"title":"The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite","authors":"J. N. Amaral, E. Borin, Dylan R. Ashley, C. Benedicto, Elliot Colp, Joao Henrique Stange Hoffmam, Marcus Karpoff, Erick Ochoa, Morgan Redshaw, R. E. Rodrigues","doi":"10.1109/ISPASS.2018.00029","DOIUrl":null,"url":null,"abstract":"A proper evaluation of techniques that require multiple training and evaluation executions of a benchmark, such as Feedback-Directed Optimization (FDO), requires multiple workloads that can be used to characterize variations on the behaviour of a program based on the workload. This paper aims to improve the performance evaluation of computer systems — including compilers, computer architecture simulation, and operating-system prototypes — that rely on the industrystandard SPEC CPU benchmark suite. A main concern with the use of this suite in research is that it is distributed with a very small number of workloads. This paper describes the process to create additional workloads for this suite and offers useful insights in many of its benchmarks. The set of additional workloads created, named the Alberta Workloads for the SPEC CPU 2017 Benchmark Suite1 is made freely available with the goal of providing additional data points for the exploration of learning in computing systems. These workloads should also contribute to ameliorate the hidden learning problem where a researcher sets parameters to a system during development based on a set of benchmarks and then evaluates the system using the very same set of benchmarks with the very same workloads.","PeriodicalId":171552,"journal":{"name":"2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPASS.2018.00029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
A proper evaluation of techniques that require multiple training and evaluation executions of a benchmark, such as Feedback-Directed Optimization (FDO), requires multiple workloads that can be used to characterize variations on the behaviour of a program based on the workload. This paper aims to improve the performance evaluation of computer systems — including compilers, computer architecture simulation, and operating-system prototypes — that rely on the industrystandard SPEC CPU benchmark suite. A main concern with the use of this suite in research is that it is distributed with a very small number of workloads. This paper describes the process to create additional workloads for this suite and offers useful insights in many of its benchmarks. The set of additional workloads created, named the Alberta Workloads for the SPEC CPU 2017 Benchmark Suite1 is made freely available with the goal of providing additional data points for the exploration of learning in computing systems. These workloads should also contribute to ameliorate the hidden learning problem where a researcher sets parameters to a system during development based on a set of benchmarks and then evaluates the system using the very same set of benchmarks with the very same workloads.