The International Conference for High Performance Computing, Networking, Storage and Analysis
A Framework for Distributed Tensor Computations.
Student: Martin D. Schatz (University of Texas at Austin)
Supervisor: Robert A. van de Geijn (University of Texas at Austin)
Abstract: Recently, data models have become more complex leading to the need for multi-dimensional representations to express the data in a more meaningful way. Commonly, tensors are used to represent such data and multi-linear algebra, the math associated with tensors, has become essential for tackling problems in big data and scientific computing. Up to now, the main approach to solving problems of multi-linear algebra has been based on mapping the multi-linear algebra to linear algebra and relying on highly efficient linear algebra libraries to perform the equivalent computation. Unfortunately, there are inherent inefficiencies associated with this approach. In this work, we define a notation for tensor computations performed on distributed-memory architectures. Additionally, we show how, using the notation, algorithms can be systematically derived, required collective communications identified, and approximate costs analyzed for a given tensor contraction operation.