Parallel computations in Java with PCJ library

Nowicki, Marek; Bała, Piotr

doi:10.1109/hpcsim.2012.6266941

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…Additionally, basing the solution on Java 7 allows for easy transition to next version of the language (so that its newly developed features could be exploited). Moreover -the library's performance has not been compromised (see below; further analysis can be found in [14], and [15]). …”

Section: Motivationmentioning

confidence: 97%

“…The rest of the paper is structured as follows: in section II an overview of PCJ library is given, together with basic usage examples (for a more detailed description see [16]); section III describes discusses the exemplary applications that has been parallelized using PCJ library. Section III-C presents performance results for parallel raytracing, section III-D presents the benchmarking of matrix multiplication and section III-E for map-reduce calculations.…”

Section: Motivationmentioning

confidence: 99%

“…The parameter indexes is optional, and can be used only for arrays, to idicate the specified element in the array to be communicated. For the detailed syntax please refer to the PCJ manual [16].…”

Section: Motivationmentioning

confidence: 99%

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PCJ - Java library for high performance computing in PGAS model

Nowicki

Górski

Grabrczyk

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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This paper presents the application of the PCJ library for the parallelization of the selected HPC applications implemented in Java language. The library is motivated by partitioned global address space (PGAS) model represented by Co-Array Fortran, Unified Parallel C, X10 or Titanium.In the PCJ, each task has its own local memory and stores and access variables locally. Variables can be shared between tasks and can be accessed, read and modified by other tasks. The library provides methods to perform basic operations like synchronization of tasks, get and put values in asynchronous onesided way. Additionally the library offers methods for creating groups of tasks, broadcasting and monitoring variables. The PCJ has ability to work on the multinode multicore systems hiding details of inter-and intranode communication. The PCJ library fully complies with Java standards therefore the programmer does not have to use additional libraries, which are not part of the standard Java distribution.In this paper the PCJ library has been used to run example HPC applications on the multicore nodes. In particular we present performance results for parallel raytracing, matrix multiplication and map-reduce calculations. The detailed information on performance of the reduction operation is also presented.The results show good performance and scalability compared to native implementations of the same algorithms. In particular, MPI C++ and Java 8 parallel streams have been used as a reference. It is noteworthy that the PCJ library due to its performance and ability to create simple code has great promise to be successful for parallelization of the HPC applications.

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Section: Motivationmentioning

confidence: 97%

Section: Motivationmentioning

confidence: 99%

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PCJ - Java library for high performance computing in PGAS model

Nowicki

Górski

Grabrczyk

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

Self Cite

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“…Another notable PGAS implementation is Parallel Computing in Java [29] (PCJ), providing a library of functions and dedicated annotations for distributed memory access over an HPC cluster. e proposed solution uses Java language constructs like classes, interfaces, and annotations for storing and exchanging common data between the cooperating processes, potentially placed in different Java Virtual Machines on separated cluster nodes.…”

Section: Partitioned Global Address Spacementioning

confidence: 99%

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

Czarnul

Proficz

Drypczewski

2020

Scientific Programming

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This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.

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“…For that purpose, we have developed the PCJ library [1]. The PCJ library introduces parallel programming capabilities using the Partitioned Global Address Space model.…”

Section: Introductionmentioning

confidence: 99%

Scalable computing in Java with PCJ Library. Improved collective operations

Nowicki¹,

Górski²,

Bała³

2021

Proceedings of International Symposium on Grids &Amp; Clouds 2021 — PoS(ISGC2021)

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Machine learning and Big Data workloads are becoming as important as traditional HPC ones. AI and Big Data users tend to use new programming languages such as Python, Julia, or Java, while the HPC community is still dominated by C/C++ or Fortran. Hence, there is a need for new programming libraries and languages that will integrate different applications and allow them to run on large computer infrastructure. Since modest computers are multinode and multicore, parallel execution is an additional challenge here. For that purpose, we have developed the PCJ library, which introduces parallel programming capabilities to Java using the Partitioned Global Address Space model. It does not modify language nor running environment (JVM). The PCJ library allows for easy development of parallel code and runs it on laptops, workstations, supercomputers, and the cloud. This paper presents an overview of the PCJ library and its usage in parallelizing selected workloads, including HPC, AI, and Big Data. The performance and scalability are presented. We present recent addition to the PCJ library, which are collective operations. The collective operations significantly reduce the number of lines of code to write, ensuring good performance.

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Parallel computations in Java with PCJ library

Cited by 17 publications

References 8 publications

PCJ - Java library for high performance computing in PGAS model

PCJ - Java library for high performance computing in PGAS model

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

Scalable computing in Java with PCJ Library. Improved collective operations

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