祝贺实验室黄一智同学的论文“A Novel Multi-CPU/GPU Collaborative Computing Framework for SGD-based Matrix Factorization”发表于ICPP会议。

International Conference on Parallel Processing (ICPP) 是中国计算机学会推荐国际学术会议 (计算机体系结构/并行与分布计算/存储系统) 中的B类会议。

论文信息：

Yizhi Huang, Yanlong Yin, Yan Liu, Shuibing He, Yang Bai, Renfa Li. A Novel Multi-CPU/GPU Collaborative Computing Framework for SGD-based Matrix Factorization. In 50th International Conference on Parallel Processing (ICPP 2021). Association for Computing Machinery, New York, NY, USA, Article 76, 1-12, DOI: 10.1145/3472456.3472520.

论文链接：

https://dl.acm.org/doi/10.1145/3472456.3472520

摘要：

This paper presents a heterogeneous collaborative computing framework for SGD-based Matrix Factorization, named HCC-MF. HCC-MF 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.