Analytical cache modeling and tilesize optimization for tensor contractions
Analytical cache modeling and tilesize optimization for tensor contractions
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DOI：
10.1145/3295500.3356218
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发表时间：
2019-11
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通讯作者：
Rui Li;Aravind Sukumaran-Rajam;R. Veras;Tze Meng Low;F. Rastello;A. Rountev;P. Sadayappan
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作者：
Rui Li;Aravind Sukumaran-Rajam;R. Veras;Tze Meng Low;F. Rastello;A. Rountev;P. Sadayappan
处理器和存储器层次结构之间的数据移动是限制现代计算机体系结构上许多应用程序性能的根本瓶颈。平铺和循环置换是提高数据局部性的关键技术。然而，由于有大量的循环，选择有效的块大小和循环排列对于张量压缩来说是特别具有挑战性的。即使是最先进的编译器通常也会产生次优的块大小和循环排列，因为它们依赖于天真的成本模型。本文提出了一种基于解析模型的张量压缩多层瓷砖尺寸优化和排列选择方法。我们的实验结果表明，该方法在张量压缩方面取得了与最先进的框架和库相当或更好的性能。
Data movement between processor and memory hierarchy is a fundamental bottleneck that limits the performance of many applications on modern computer architectures. Tiling and loop permutation are key techniques for improving data locality. However, selecting effective tile-sizes and loop permutations is particularly challenging for tensor contractions due to the large number of loops. Even state-of-the-art compilers usually produce sub-optimal tile-sizes and loop permutations, as they rely on naïve cost models. In this paper we provide an analytical model based approach to multi-level tile size optimization and permutation selection for tensor contractions. Our experimental results show that this approach achieves comparable or better performance than state-of-the-art frameworks and libraries for tensor contractions.