High-performance dense tucker decomposition on GPU clusters

Abstract

The dense Tucker decomposition method is one of the most popular algorithms for analyzing and compressing data with multi-way relationship. Its execution time is typically dominated by dense matrix multiplication operations, which makes it well-suited for GPU acceleration. State-of-the-art distributed dense Tucker implementations for CPU clusters adopt multi-dimensional partitioning that optimizes for storage and communication. This, however, leads to smaller matrix dimensions that result in under-utilizing the GPU resources. In this paper, we present our optimized implementation and performance analysis of dense Tucker decomposition on a multi-GPU cluster. We propose three key optimizations: a new partitioning strategy that improves performance for GPUs, a new tensor matricization layout that halves the number of communication and matricization steps, and a variation of the randomized SVD algorithm to overcome the eigenvalue calculation bottleneck that arises from the high speedup gained from GPU acceleration. When compared to the state-of-the-art TuckerMPI library, our best GPU implementation, which employs all three optimizations described above, achieves up to 11.8× speedup on 64 nodes. Our best CPU implementation, which also employs all three optimizations, achieves up to 3.6× speedup over TuckerMPI on 64 nodes. When we compare our best GPU implementation to our best CPU implementation, the speedup ranges from 2.1× to 3.6× on a single node, and from 1.8× to 3.3× on 64 nodes, depending on the input data set.