FlinkCL: an OpenCL-based in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. Research on in-memory big data management and processing has been prompted by the increase in main memory capacity and the explosion in big data. By offering an efficient in-memory distributed execution model, existing in-memory cluster computing platforms such as Flink and Spark have been proven to be outstanding for processing big data. This paper proposes FlinkCL, an in-memory computing architecture on heterogeneous CPU-GPU clusters based on OpenCL that enables Flink to utilize GPU’s massive parallel processing ability. Our proposed architecture utilizes four techniques: a heterogeneous distributed abstract model (HDST), a Just-In-Time (JIT) compiling schema, a hierarchical partial reduction (HPR) and a heterogeneous task management strategy. Using FlinkCL, programmers only need to write Java code with simple interfaces. The Java code can be compiled to OpenCL kernels and executed on CPUs and GPUs automatically. In the HDST, a novel memory mapping scheme is proposed to avoid serialization or deserialization between Java Virtual Machine (JVM) objects and OpenCL structs. We have comprehensively evaluated FlinkCL with a set of representative workloads to show its effectiveness. Our results show that FlinkCL improve the performance by up to 11× for some computationally heavy algorithms and maintains minor performance improvements for a I/O bound algorithm.
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References in zbMATH (referenced in 2 articles , 1 standard article )
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- Zhang, Longxin; Zhou, Liqian; Salah, Ahmad: Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments (2020)
- Chen, Cen; Li, Kenli; Ouyang, Aijia; Li, Keqin: FlinkCL: an OpenCL-based in-memory computing architecture on heterogeneous CPU-GPU clusters for big data (2018)