Efficient Fiber Operation of High Ranked Tensors for Dynamic Dataset

Sarjil Shariar, K. M. Azharul Hasan, Mehnuma Tabassum Omar
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Abstract

Multidimensional data is available in all respects around us, therefore creating the essentiality of high ranked tensors. We will construct a data structure on primary memory to contain the high order tensors in a shape of two dimensional array. The tensor will be shaped with dynamic dataset i.e. data can be compacted later with a data structure capable of expanding. The focus of this paper is to estimate the efficiency of this Stretching Data Structure (SDS) comparing with Traditional Data Structure (TDS) based on fiber operation of high ranked tensors. Because of efficient indexing in SDS we will get a faster data retrieval on fiber. With the increase in dimension the efficiency rate is expected to get improved. Large and complex computation with high order tensors can require immense time hence this research will assist to conduct those computation in a feasible way.
动态数据集高阶张量的高效光纤运算
多维数据在我们周围的各个方面都是可用的,因此创造了高阶张量的必要性。我们将在主存上构造一个数据结构,以二维数组的形式包含高阶张量。张量将使用动态数据集进行塑形,即数据可以稍后使用能够扩展的数据结构进行压缩。本文的重点是比较基于高阶张量的光纤运算的拉伸数据结构(SDS)与传统数据结构(TDS)的效率。由于SDS的高效索引,我们将获得更快的光纤数据检索速度。随着尺寸的增加,效率有望得到提高。大而复杂的高阶张量计算需要大量的时间,因此本研究将有助于以可行的方式进行这些计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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