PCJ is a library for Java language that helps to perform parallel and distributed calculations. The current version is able to work on the multicore systems connected with the typical interconnect such as ethernet or infiniband providing users with the uniform view across nodes.
Download PCJ library (jar file of 29.04.2017 ver. 5.0.6) Latest (bug fixing release)!
Download PCJ manual (pdf) for PCJ 5 New!
The PCJ library can be used with no cost at BSD license. It requires Java 8 and no additional tools or comilers. The PCJ library for Java 7 is available in the dowload section.
The source code is available at GitHub: https://github.com/hpdcj/pcj
Version 5 introduces asyncPut() and asyncGet() methods; put() and get() methods are now synchronous. There is new handling of shared variables. The code developed for PCJ 4 hast to be modified. For details please reffer to the JavaDoc file.
The usage should be acknowledged by reference to the PCJ web site and/or reference to the papers:
- M. Nowicki, Ł. Górski, P. Bała PCJ – Java Library for Highly Scalable HPC and Big Data Processing 2018 International Conference on High Performance Computing \& Simulation (HPCS), pp:12-20 IEEE, 2018
- M. Nowicki, M. Ryczkowska, Ł. Górski, M. Szynkiewicz, P. Bała PCJ - a Java library for heterogenous parallel computing In: X. Zhuang (Ed.) Recent Advances in Information Science (Recent Advances in Computer Engineering Series vol 36) WSEAS Press 2016 pp. 66-72
- M. Nowicki, Ł. Górski, P. Grabarczyk, P. Bała PCJ - Java library for high performance computing in PGAS model In: W. W. Smari and V. Zeljkovic (Eds.) 2012 International Conference on High Performance Computing and Simulation (HPCS) IEEE 2014 pp. 202-209
- M. Nowicki, P. Bała PCJ-new approach for parallel computations in java In: P. Manninen, P. Oster (Eds.) Applied Parallel and Scientific Computing, LNCS 7782, Springer, Heidelberg (2013) pp. 115-125
- M. Nowicki, P. Bała Parallel computations in Java with PCJ library In: W. W. Smari and V. Zeljkovic (Eds.) 2012 International Conference on High Performance Computing and Simulation (HPCS) IEEE 2012 pp. 381-387
Full paper list can be found here: http://pcj.icm.edu.pl/pcj-papers
Contact: bala@icm.edu.pl faramir@icm.edu.pl
π Approximation using Monte Carlo
The performance of the code to approximate π using Monte Carlo method. The code has been executed on the PC cluster halo2 at ICM. The red line shows ideal scalling.