SwTensor: accelerating tensor decomposition on Sunway architecture

Abstract

Modern applications are digesting and generating data with rich features that are stored in high dimensional array or tensor. The computation applied to tensor, such as Canonical Polyadic decomposition (CP decomposition) plays an important role in understanding the internal relationships within the data. Using CP decomposition to analyze large tensor with billions of sizes requires tremendous computation power. In the meanwhile, the emerging Sunway many-core processor has demonstrated its computation advantage in powering the first hundred petaFLOPS supercomputer in the world. In this paper, we propose swTensor that adapts the CP decomposition to Sunway processor by leveraging the MapReduce framework for automatic parallelization and the unique architecture of Sunway for high performance. Specifically, we divide the major computation of CP decomposition into four sub-procedures and implement each using MapReduce framework with customized design key-value pair. Also, we tile the data during the computation so that it fits into the limited local device memory on Sunway for better performance. Moreover, we propose a performance auto-tuning mechanism to search for the optimal parameter settings in swTensor. The experimental results demonstrate swTensor achieves better performance than the state-of-the-art BigTensor and CSTF with the average speedup of 1.36 $$\times $$ and 1.24 $$\times $$, respectively. Besides, swTensor exhibits better scalability when scaling across multiple Sunway processors.