DocumentCode :
2917326
Title :
Efficient and scalable computations with sparse tensors
Author :
Baskaran, Mani ; Meister, B. ; Vasilache, N. ; Lethin, R.
Author_Institution :
Reservoir Labs. Inc., New York, NY, USA
fYear :
2012
fDate :
10-12 Sept. 2012
Firstpage :
1
Lastpage :
6
Abstract :
For applications that deal with large amounts of high dimensional multi-aspect data, it becomes natural to represent such data as tensors or multi-way arrays. Multi-linear algebraic computations such as tensor decompositions are performed for summarization and analysis of such data. Their use in real-world applications can span across domains such as signal processing, data mining, computer vision, and graph analysis. The major challenges with applying tensor decompositions in real-world applications are (1) dealing with large-scale high dimensional data and (2) dealing with sparse data. In this paper, we address these challenges in applying tensor decompositions in real data analytic applications. We describe new sparse tensor storage formats that provide storage benefits and are flexible and efficient for performing tensor computations. Further, we propose an optimization that improves data reuse and reduces redundant or unnecessary computations in tensor decomposition algorithms. Furthermore, we couple our data reuse optimization and the benefits of our sparse tensor storage formats to provide a memory-efficient scalable solution for handling large-scale sparse tensor computations. We demonstrate improved performance and address memory scalability using our techniques on both synthetic small data sets and large-scale sparse real data sets.
Keywords :
data analysis; tensors; data analysis; data reuse optimization; data summarization; high dimensional multiaspect data; large-scale sparse real data sets; multilinear algebraic computations; multiway arrays; scalable computations; sparse tensor storage formats; sparse tensors; synthetic small data sets; tensor decomposition algorithms; tensor decompositions; Matrix decomposition; Memory management; Optimization; Sparse matrices; Standards; Tensile stress; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High Performance Extreme Computing (HPEC), 2012 IEEE Conference on
Conference_Location :
Waltham, MA
Print_ISBN :
978-1-4673-1577-7
Type :
conf
DOI :
10.1109/HPEC.2012.6408676
Filename :
6408676
Link To Document :
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