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.