Evaluation of the Parallel Performance of the Java and PCJ on the Intel KNL Based Systems

Nowicki, Marek; Górski, Łukasz; Bała, Piotr

doi:10.1007/978-3-319-78054-2_27

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Scalability is better for larger arrays as it was presented in our previous work. 5 As in the previous case, virtualization provides no significant overhead. The AWS cloud shows also good scalability.…”

Section: Fast Fourier Transformmentioning

confidence: 84%

“…3 The performance comparison with the C/MPI based codes has been presented in previous papers. 4,5 The extensive comparison with Java-based solutions including APGAS (Java implementation of X10 language) has been also performed. 3,6 In this article, we contribute by focusing on the performance of selected applications run on Amazon web services (AWS) elastic compute cloud (EC2) using both Intel and ARM architectures, and Linaro Developer Cloud that provides instances with ARM processors.…”

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confidence: 99%

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Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version)

Nowicki

Górski

Bała

2021

Concurrency and Computation

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Large-scale computing and data processing with cloud resources is gaining popularity.However, the usage of the cloud differs from traditional high-performance computing (HPC) systems and both algorithms and codes have to be adjusted. This work is often time-consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (parallel computing in Java), a novel tool for scalable HPC and big data processing in Java. In this article, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. The performance evaluation is based on the examples of highly scalable applications of different characteristics focusing on CPU, communication or I/O. They run on the traditional HPC system and Amazon web services Cloud as well as Linaro Developer Cloud. For the clouds, we have used Intel x86 and ARM processors for running Java codes without changing any line of the program code and without the need for time-consuming recompilation. Presented applications have been parallelized using the partitioned global address space programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a performance close to HPC systems.

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Section: Fast Fourier Transformmentioning

confidence: 84%

mentioning

confidence: 99%

Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version)

Nowicki

Górski

Bała

2021

Concurrency and Computation

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“…Nowicki et al [18] present a library for Java called PCJ, whose purpose is to easily allow parallel computation. This library is evaluated on a KNL-based platform and the authors come to the conclusion that Java can run successfully on architectures designed for parallelism.…”

Section: Java and Big Data Processing Tools On Hpc Systemsmentioning

confidence: 99%

A Benchmarking Study to Evaluate Apache Spark on Large-Scale Supercomputers

Thiruvathukal,

Christensen,

Jin

et al. 2019

Preprint

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As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly dependent on sophisticated dataflows and out-of-core methods for efficient system utilization. In addition, as HPC systems grow, memory access and data sharing are becoming performance bottlenecks. Cloud computing employs a data processing paradigm typically built on a loosely connected group of low-cost computing nodes without relying upon shared storage and/or memory. Apache Spark is a popular engine for large-scale data analysis in the cloud, which we have successfully deployed via job submission scripts on production clusters.In this paper, we describe common parallel analysis dataflows for both Message Passing Interface (MPI) and cloud based applications. We developed an effective benchmark to measure the performance characteristics of these tasks using both types of systems, specifically comparing MPI/C-based analyses with Spark. The benchmark is a data processing pipeline representative of a typical analytics framework implemented using mapreduce. In the case of Spark, we also consider whether language plays a role by writing tests using both Python and Scala, a language built on the Java Virtual Machine (JVM). We include performance results from two large systems at Argonne National Laboratory including THETA, a Cray XC40 supercomputer on which our experiments run with 65,536 cores (1024 nodes with 64 cores each). The results of our experiments are discussed in the context of their applicability to future HPC architectures. Beyond understanding performance, our work demonstrates that technologies such as Spark, while typically aimed at multi-tenant cloud-based environments, show promise for data analysis needs in a traditional clustering/supercomputing environment.

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“…PCJ implements the Partitioned Global Address Space (PGAS) paradigm for running concurrent applications. The PCJ library allows running concurrent applications on systems comprise one or many multicore nodes like standard workstation, nodes with hundreds of computational threads like Intel KNL processors [11], computing clusters or even supercomputers [12].…”

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confidence: 99%

Comparison of sort algorithms in Hadoop and PCJ

Nowicki

2020

J Big Data

Self Cite

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Sorting algorithms are among the most commonly used algorithms in computer science and modern software. Having efficient implementation of sorting is necessary for a wide spectrum of scientific applications. This paper describes the sorting algorithm written using the partitioned global address space (PGAS) model, implemented using the Parallel Computing in Java (PCJ) library. The iterative implementation description is used to outline the possible performance issues and provide means to resolve them. The key idea of the implementation is to have an efficient building block that can be easily integrated into many application codes. This paper also presents the performance comparison of the PCJ implementation with the MapReduce approach, using Apache Hadoop TeraSort implementation. The comparison serves to show that the performance of the implementation is good enough, as the PCJ implementation shows similar efficiency to the Hadoop implementation.

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Evaluation of the Parallel Performance of the Java and PCJ on the Intel KNL Based Systems

Cited by 4 publications

References 6 publications

Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version)

Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version)

A Benchmarking Study to Evaluate Apache Spark on Large-Scale Supercomputers

Comparison of sort algorithms in Hadoop and PCJ

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