Performance Evaluation Of Parallel Sparse Tensor Decomposition Implementations

Tensor decomposition, the higher-order analogue to singular value decomposition, has emerged as a useful tool for finding relationships in large, sparse, multidimensional data sets. As this technique matures and is applied to increasingly larger data sets, the need for high performance implementations becomes critical. In this work, we perform an objective empirical evaluation of three popular parallel implementations of the Candecomp/Parafac Alternating Least Squares (CP-ALS) tensor decomposition algorithm, namely SPLATT, DFacTo, and ENSIGN. We conduct performance studies across a variety of data sets, comparing the total memory required, the runtime, and the parallel scalability of each implementation. We find that the approach taken by SPLATT results in the fastest runtimes across the data sets, performing 5 - 22.64 times faster than the other tools. Additionally, SPLATT consumes 1.16 - 8.62 times less memory than the other tools. When tested on up to 20 cores or nodes, SPLATT using distributed memory parallelism exhibits the best strong scaling.