论文标题
稀疏张量转座
Sparse Tensor Transpositions
论文作者
论文摘要
我们提出了一种用于转座稀疏张量的新算法，称为Quesadilla。该算法将稀疏张量数据结构转换为坐标列表，并使用快速的多通辐射算法对其进行分组，该算法利用了所需的换位的知识，并将张量的部分坐标顺序订购以可降低并行部分分类通过的数量。我们在弗罗斯特（Frostt Collection）的19个张量中评估了Quesadilla的串行和并行实现，这是一组来自科学和数据分析应用程序的张量。我们将Quesadilla和Top-2-Sadilla与几种最新方法进行比较，包括在Splatt Tensor分解库中使用的张量转置常规。在串行测试中，玉米饼是所有张量和转置组合中60％的最佳策略，在一半的组合中，Splatt的策略至少提高了19％。在并行测试中，玉米饼或Top-2-Sadilla的至少一个是所有张量和转置组合的最佳策略。
We present a new algorithm for transposing sparse tensors called Quesadilla. The algorithm converts the sparse tensor data structure to a list of coordinates and sorts it with a fast multi-pass radix algorithm that exploits knowledge of the requested transposition and the tensors input partial coordinate ordering to provably minimize the number of parallel partial sorting passes. We evaluate both a serial and a parallel implementation of Quesadilla on a set of 19 tensors from the FROSTT collection, a set of tensors taken from scientific and data analytic applications. We compare Quesadilla and a generalization, Top-2-sadilla to several state of the art approaches, including the tensor transposition routine used in the SPLATT tensor factorization library. In serial tests, Quesadilla was the best strategy for 60% of all tensor and transposition combinations and improved over SPLATT by at least 19% in half of the combinations. In parallel tests, at least one of Quesadilla or Top-2-sadilla was the best strategy for 52% of all tensor and transposition combinations.
