Big data

Linguistics

Philosophy

Locality

Memory management

Embedded system

CUDA

GPU cluster

Scheduling (production processes)

Operating system

Programming paradigm

Economics

Parallel computing

Computer science

Visual arts

Distributed computing

Architecture

Computer architecture

Supercomputer

Bottleneck

Semiconductor memory

Operations management

Programming language

Central processing unit

IEEE Transactions on Parallel and Distributed Systems

The increasing main memory capacity and the explosion of big data have fueled the development of in-memory big data management and processing. By offering an efficient in-memory parallel execution model which can eliminate 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. This paper proposes 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 have proposed a programming framework based on Flink's abstract model, i.e., DataSet (DST), hiding the programming complexity of GPUs behind the simple and familiar high-level interfaces. To achieve high performance and good load-balance, an efficient JVM-GPU communication strategy, a GPU cache scheme, and an adaptive locality-aware scheduling scheme for three-stage pipelining execution are proposed. Extensive experiment results indicate that the high computational power of GPUs can be efficiently utilized, and the implementation on GFlink outperforms that on the original CPU-based Flink.

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