使用子集基准套件进行实验

Jabatan Perangkaan, Malaysia Kenyataan, Sebut Harga, Tawaran adalah dipelawa, daripada syarikat-syarikat, tempatan yang berdaftar dengan, Kementerian Kewangan, Samihah binti Kamaruddin
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引用次数: 27

摘要

基准测试是比较计算系统性能的最流行的工具之一。基准套件通常包含多个具有或多或少相同属性的基准程序。因此,套件包含冗余,这会增加执行或模拟基准套件的成本,而不会增加价值。为了限制模拟时间,研究人员经常对基准套件进行子集化。然而,正确识别具有代表性的子集对于执行可信评估至关重要。本文表明,以一种保持基准套件的代表性的方式对基准套件进行子集是非常重要的。我们展示了一个小的随机选择的子集并不代表填充基准套件。我们讨论了speccpu2000基准测试集子集的算法,并表明它们比随机选择的子集提供了更有代表性的子集。然而,本文评估的算法并不总是计算具有代表性的子集:算法对某些子集大小产生不好的结果。从这个意义上说,这些算法是不可靠的,因为仍然需要验证基准套件子集。我们找到了一个可靠的子集算法。然而,在其他情况下,该算法是否同样可靠尚不确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experiments with subsetting benchmark suites
Benchmarks are one of the most popular tools to compare the performance of computing systems. Benchmark suites typically contain multiple benchmark programs with more or less the same properties. Hence the suite contains redundancy, which increases the cost of executing or simulating the benchmark suite without adding value. To limit simulation time, researchers frequently subset benchmark suites. However, correctly identifying a representative subset is of paramount importance to perform a trustworthy evaluation. This paper shows that subsetting a benchmark suite in such a way that representativeness of the suite is maintained is non-trivial. We show that a small randomly selected subset is not representative of the fill benchmark suite. We discuss algorithms to subset the SPEC CPU 2000 benchmark suite and show that they provide more representative subsets than randomly selected subsets. However, the algorithms evaluated in this paper do not always compute representative subsets: the algorithms produce bad results for some subset sizes. In this sense, these algorithms are unreliable, as it remains necessary to validate the benchmark suite subset. We find one subsetting algorithm that is reliable. It is, however, uncertain whether this algorithm is also reliable under other circumstances.
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