A W-cycle algorithm for efficient batched SVD on GPUs

Abstract

As a fundamental factorization operation, the singular value decomposition (SVD) plays a paramount role in abroad range of domains such as scientific computing and machine learning. Due to its computational bottleneck of factorization for small matrices in real-world applications, many GPU-accelerated batched SVD algorithms have been investigated recently. However, these algorithms failed to achieve a balance between data locality and parallelism because their workflows depend on the size of each matrix. In this work, we propose a matrix-size-independent W-cycle algorithm to accelerate the batched one-side Jacobi SVD on GPUs, which successfully strikes the balance between data locality and parallelism. The experimental evaluation demonstrates that the proposed algorithm achieves 4.5X performance speedup on average over the state-of-the-art cuSOLVER.