GFlink: An In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data

Cen Chen, Kenli Li, Aijia Ouyang, Zhuo Tang, Keqin Li

Abstract: The increasing main memory capacity and the explosion of big data has fueled the development of in-memory big data management and processing. By offering efficient in-memory parallel execution model which eliminates disk I/O bottleneck, existing in-memory cluster computing platforms (e.g., Flink and Spark) have already been proven to be outstanding platforms for big data processing. However, these platforms are merely CPU-based systems now. This paper has proposed GFlink, an in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. Our proposed architecture extends the original Flink from CPU clusters to heterogeneous CPU-GPU clusters, greatly improving the computational power of Flink. Furthermore, we proposed an abstract GPU-based model named as GDST, hiding the programming complexity of GPUs behind the simple and familiar high-level interfaces, and automatically managing task partitioning, device memory, and parallelization on MultiCore GPUs. To achieve high performance and good load-balance, an efficient JVM-GPU communication strategy and an adaptive locality-aware scheduling scheme for three-stage pipeline execution are proposed. Extensive experiment results indicate that not only the high computational power of GPUs can be efficiently utilized, but also the implementations on GFlink outperforms that on the original CPU-based Flink.

External IDs:dblp:conf/icpp/ChenLOTL16