计算机科学
张量(固有定义)
塔克分解
管道(软件)
张量分解
稀疏矩阵
分段
数据压缩
计算科学
并行计算
图形处理单元
稀疏逼近
分解
基质(化学分析)
钥匙(锁)
绘图
算法
数学
计算机图形学(图像)
物理
数学分析
生物
复合材料
高斯分布
量子力学
计算机安全
材料科学
程序设计语言
纯数学
生态学
作者
Yang Wangdong,Keqin Li,Keqin Li
出处
期刊:ACM Transactions on Knowledge Discovery From Data
[Association for Computing Machinery]
日期:2019-11-11
卷期号:13 (6): 1-27
被引量:18
摘要
Tensors have drawn a growing attention in many applications, such as physics, engineering science, social networks, recommended systems. Tensor decomposition is the key to explore the inherent intrinsic data relationship of tensor. There are many sparse tensor and vector multiplications (SpTV) in tensor decomposition. We analyze a variety of storage formats of sparse tensors and develop a piecewise compression strategy to improve the storage efficiency of large sparse tensors. This compression strategy can avoid storing a large number of empty slices and empty fibers in sparse tensors, and thus the storage space is significantly reduced. A parallel algorithm for the SpTV based on the high-order compressed format based on slices is designed to greatly improve its computing performance on graphics processing unit. Each tensor is cut into multiple slices to form a series of sparse matrix and vector multiplications, which form the pipelined parallelism. The transmission time of the slices can be hidden through pipelined parallel to further optimize the performance of the SpTV.
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