The article discusses the application of HPJava language for parallel computing and various rules about use of distributed arrays in HPJava programes. We illustrated how to build a Beowulf system under Linux and HPJava and use matrix multiplication parallel algorithm module design to test this Beowulf system.

Application of HPJava in parallel computing

Designing a simple but powerful low-level communication library for Java HPC environments is an important task. We introduce new low-level communication library for Java HPC, called mpjdev. The mpjdev API is designed with the goal that it can be implemented portably on network platforms and efficiently on parallel hardware. Unlike MPI which is intended for the application developer, mpjdev is meant for library developers. Application level communication may be implemented on top of mpjdev. The mpjdev API itself might be implemented on top of Java sockets in a portable network implementation, or-on HPC platforms-through a JNI (Java Native Interface) to a subset of MPI.

/pdf/a-low-level-communication-library-for-java-hpc-4yt5y012gs.pdf

A low–level communication library for java HPC

This dissertation is concerned with efficient compilation of our Java-based, high-performance, library-oriented, SPMD style, data parallel programming language: HPJava. 
It starts with some historical review of data-parallel languages such as High Performance Fortran (HPF), message-passing frameworks such as p4, PARMACS, and PVM, as well as the MPI standard, and high-level libraries for multiarrays such as PARTI, the Global Array (GA) Toolkit, and Adlib. 
Next, we will introduce our own programming model, which is a flexible hybrid of HPF-like data-parallel language features and the popular, library-oriented, SPMD style. We refer to this model as the HPspmd programming model . We will overview the motivation, the language extensions, and an implementation of our HPspmd programming language model, called HPJava. HPJava extends the Java language with some additional syntax and pre-defined classes for handling multiarrays, and a few new control constructs. We discuss the compilation system, including HPspmd classes, type-analysis, pre-translation, and basic translation scheme. In order to improve the performance of the HPJava system, we discuss optimization strategies we will apply such as Partial Redundancy Elimination, Strength Reduction, Dead Code Elimination, and Loop Unrolling. We experiment with and benchmark large scientific and engineering HPJava programs on Linux machine, shared memory machine, and distributed memory machine to prove our compilation and proposed optimization schemes are appropriate for the HPJava system. 
Finally, we will compare our HPspmd programming model with modern related languages including Co-Array Fortran, ZPL, JavaParty, Timber, and Titanium.

Towards efficient compilation of the hpjava language for high performance computing

HPJava is an environment for scientific and parallel programming using Java. It is based on an extended version of the Java language. One feature that HPJava adds to Java is a multi-dimensional array, or multiarray, with properties similar to the arrays of Fortran. We are using Adlib as our high-level collective communication library. Adlib was originally developed using C++ by the Parallel Compiler Runtime Consortium (PCRC). Many functionalities of this high-level communication library is following its predecessor. However, many design issues are reconsidered and re-implemented according to Java environment. Detailed functionalities and implementation issues of this collective library will be described.

/pdf/collective-communications-for-scalable-programming-1j855c0rn1.pdf

Collective communications for scalable programming

/pdf/platforms-for-hpjava-runtime-support-for-scalable-44lgm8ywr4.pdf

Platforms for hpjava: runtime support for scalable

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Platforms for hpjava: runtime support for scalable