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Similar Samples Cleaning in Speculative Multithreading

机译：类似的样品在推测多线程中清洁

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Speculative multithreading (SpMT) is a thread-level automatic paral-lelization technique to accelerate sequential programs on multi-core. Too large and too dense samples can not be able to effectively promote the effectiveness of thread partition, parallel thread evaluation, etc. Selection of appropriate samples is of vital importance. The appropriateness reflects in two points. First, redundant samples never exist. Second, similarity between any two samples is not high. We express a sample with one feature vector of fixed length. We extract sample feature vectors using profiler in Prophet during compile time when running programs. Such profiles are created by feature extraction routines which map each program onto a tuple (N_1, N_2, N_3, N_4, N_5, N_6) where Ni is a count of an occurrence of a particular feature. A comparison routine is then invoked which detects similarities amongst tuples. According to the program features, similarity values between samples are calculated to assess the similar degree. In this paper, we introduce a novel way of assessing the similarity of two program samples using Theory of Fuzzy. We firstly calculate the Euclidean Distance of two different program samples as the input, and then assess the overall similarity degrees as well as respective similarity degrees, using corresponding Fuzzy Functions. Based on them, we clean the similar samples. With multidimensional samples generated virtually, we get that average density of samples decreases, so that a more effective collection of samples are created.
机译：投机性多线程（SPMT）是一种螺纹级别自动探测技术，可加速多核的顺序程序。太大致密的样品无法有效地促进线分区的有效性，并行线程评估等。选择适当的样本是至关重要的。适当性反映了两点。首先，冗余样本永远不会存在。其次，任何两个样本之间的相似性不高。我们用一个特征向量的固定长度向一个特征向量表达一个样本。在运行程序时，我们在Propile Time期间将示例特征向量提取在先知中使用Profiler。这种配置文件由特征提取例程创建，该特征提取例程将每个程序映射到元组（n_1，n_2，n_3，n_4，n_5，n_6），其中Ni是特定特征的发生的计数。然后调用比较例程，该例程检测到元组之间的相似性。根据程序特征，计算样本之间的相似性值以评估类似程度。在本文中，我们使用模糊理论介绍了评估两个程序样本的相似性的新方法。我们首先使用相应的模糊函数计算两个不同程序样本的欧几里德距离，然后评估总相似度以及相应的相似度。基于它们，我们清洁类似的样本。通过几乎生成的多维样本，我们得到了平均的样品密度降低，从而创建了更有效的样本集合。

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