Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems
Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang, Wenjie Zhang, Xuemin Lin · 2021
Matrix Factorization (MF) has been widely applied in machine learning and data mining. Due to the large computational cost of MF, we aim to improve the efficiency of SGD-based MF computation by utilizing the massive parallel processing power of heterogeneous multiprocessors. The main challenge in parallel SGD algorithms on heterogeneous CPU-GPU systems lies in the strategy to assign tasks. We design a novel strategy to divide the matrix into a set of blocks by considering two aspects. First, we observe that the matrix should be divided nonuniformly, and relatively large blocks should be assigned to GPUs to saturate the computing power of GPUs. In addition to exploiting the characteristics of hardware, the workloads assigned to two types of hardware should be balanced. We design a cost model tailored for our problem to accurately estimate the performance of hardware on different data sizes. Extensive experiments show that our proposed algorithm achieves high efficiency with a high quality of training quality.