A Novel Multi-CPU/GPU Collaborative Computing Framework for SGD-based Matrix Factorization

This paper presents a heterogeneous collaborative computing framework for SGD-based Matrix Factorization, named HCC-MF. HCCMF can train the feature matrix efficiently using multiple CPUs and GPUs. It performs collaborative computing with data parallelism, where a server CPU is in charge of management and synchronization and other heterogeneous worker CPUs and worker GPUs performs calculation with their data assignments. HCC-MF adopts two data partition strategies, "data partition with heterogeneous load balance" and "data partition with hidden synchronization." We build a time cost model to guide the data distribution among multiple workers and we design several communication optimization techniques with consideration of datasets' and processors' characteristics. Experimental results indicate that HCC-MF can utilize more than 88% of the platform's computing power, yielding a speedup of 2.9 compared with advanced SGD-based MF, CuMF_SGD, on large-scale data sets.