HPSVM: Heterogeneous Parallel SVM with Factorization Based IPM Algorithm on CPU-GPU Cluster

Abstract

Support vector machine (SVM) is a supervised method widely used in the statistical classification and regression analysis. SVM training can be solved via the interior point method (IPM) with the advantages of low storage, fast convergence and easy parallelization. However, it is still confronted with the challenges of training speed and memory use. In this paper, we propose a parallel primal-dual IPM algorithm based on the incomplete Cholesky factorization (ICF) for efficiently training large-scale SVMs, named HPSVM, on CPU-GPU cluster. Our approach is distinguished from earlier work in that it is specifically designed to take maximal advantage of the CPU-GPU collaborative computation with the dual buffers 3-stage pipeline mechanism, and efficiently handles large-scale training datasets. In HPSVM, the heterogeneous hierarchical memory is fully explored to alleviate the bottleneck for optimizing data transfer, and the programming paradigm is presented to build an efficient collaboration mechanism between CPU and GPU. Comprehensive experiments show that HPSVM is up to 11 times faster than the CPU version on real datasets.