Nime: a native in-memory compute framework for cluster computing

Due to the increasing demand for cluster computing, various data analytics frameworks have been proposed and Apache Spark is a widely used open-source framework. It divides the program into various tasks and leverages executors on different machines for parallel task processing. However, executors r...

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Published in:	Cluster computing Vol. 28; no. 7; p. 434
Main Authors:	Chen, Chao, Wang, Zhenghua, Jiang, Chen, Wang, Zheng
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.09.2025 Springer Nature B.V
Subjects:	Benchmarks Breakdowns Clusters Compilers Computation Computer Communication Networks Computer Science Data processing Datasets Executors Java Machine learning Operating Systems Performance evaluation Processor Architectures Sanitation services Virtual environments In-memory computing Spark Distributed computing Cluster computing
ISSN:	1386-7857, 1573-7543
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Abstract	Due to the increasing demand for cluster computing, various data analytics frameworks have been proposed and Apache Spark is a widely used open-source framework. It divides the program into various tasks and leverages executors on different machines for parallel task processing. However, executors run on top of Java virtual machines (JVMs), which incurs a significant runtime overhead in terms of memory and compute resources and thus deteriorates the system’s performance. In this paper, we present NIME–a native in-memory compute framework for cluster computing–that aims to perform parallel task processing using native executors. The key idea is that NIME starts off with native manager and worker processes without JVMs. In addition, a dedicated scheduler combines data partitions for efficient processing without interruptions and a cached is leveraged for iterative computations. We evaluate the effectiveness of NIME on a compute cluster using the HiBench benchmark suite and compare the results with those from the Spark framework. Evaluation results indicate that compared to Spark, on average NIME achieves a 6.82 × speedup, while simultaneously reducing the memory usage by 84.69%. In addition, the execution speedup and memory reduction can reach up to 12.36 × and 93.97%, respectively. Together with an in-depth analysis, we show that by discarding the JVM, NIME significantly accelerates task executions and minimizes the compute and memory resource overheads.
AbstractList	Due to the increasing demand for cluster computing, various data analytics frameworks have been proposed and Apache Spark is a widely used open-source framework. It divides the program into various tasks and leverages executors on different machines for parallel task processing. However, executors run on top of Java virtual machines (JVMs), which incurs a significant runtime overhead in terms of memory and compute resources and thus deteriorates the system’s performance. In this paper, we present NIME–a native in-memory compute framework for cluster computing–that aims to perform parallel task processing using native executors. The key idea is that NIME starts off with native manager and worker processes without JVMs. In addition, a dedicated scheduler combines data partitions for efficient processing without interruptions and a cached is leveraged for iterative computations. We evaluate the effectiveness of NIME on a compute cluster using the HiBench benchmark suite and compare the results with those from the Spark framework. Evaluation results indicate that compared to Spark, on average NIME achieves a 6.82 × speedup, while simultaneously reducing the memory usage by 84.69%. In addition, the execution speedup and memory reduction can reach up to 12.36 × and 93.97%, respectively. Together with an in-depth analysis, we show that by discarding the JVM, NIME significantly accelerates task executions and minimizes the compute and memory resource overheads. Due to the increasing demand for cluster computing, various data analytics frameworks have been proposed and Apache Spark is a widely used open-source framework. It divides the program into various tasks and leverages executors on different machines for parallel task processing. However, executors run on top of Java virtual machines (JVMs), which incurs a significant runtime overhead in terms of memory and compute resources and thus deteriorates the system’s performance. In this paper, we present NIME–a native in-memory compute framework for cluster computing–that aims to perform parallel task processing using native executors. The key idea is that NIME starts off with native manager and worker processes without JVMs. In addition, a dedicated scheduler combines data partitions for efficient processing without interruptions and a cached is leveraged for iterative computations. We evaluate the effectiveness of NIME on a compute cluster using the HiBench benchmark suite and compare the results with those from the Spark framework. Evaluation results indicate that compared to Spark, on average NIME achieves a 6.82× speedup, while simultaneously reducing the memory usage by 84.69%. In addition, the execution speedup and memory reduction can reach up to 12.36× and 93.97%, respectively. Together with an in-depth analysis, we show that by discarding the JVM, NIME significantly accelerates task executions and minimizes the compute and memory resource overheads.
ArticleNumber	434
Author	Jiang, Chen Wang, Zheng Wang, Zhenghua Chen, Chao
Author_xml	– sequence: 1 givenname: Chao surname: Chen fullname: Chen, Chao organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences – sequence: 2 givenname: Zhenghua surname: Wang fullname: Wang, Zhenghua email: nopass@mail.ustc.edu.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, School of Software Engineering, University of Science and Technology of China – sequence: 3 givenname: Chen surname: Jiang fullname: Jiang, Chen organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, School of Software Engineering, University of Science and Technology of China – sequence: 4 givenname: Zheng surname: Wang fullname: Wang, Zheng email: zheng.wang@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
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SubjectTerms	Benchmarks Breakdowns Clusters Compilers Computation Computer Communication Networks Computer Science Data processing Datasets Executors Java Machine learning Operating Systems Performance evaluation Processor Architectures Sanitation services Virtual environments
Title	Nime: a native in-memory compute framework for cluster computing
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