Parallel Computing in Java

The Java programming language and environment is inspiring new research activities in many areas of computing, of which parallel computing is one of the major interests. Parallel techniques are themselves finding new uses in cluster computing systems. Although there are excellent software tools for scheduling, monitoring and message-based programming on parallel clusters, these systems are not yet well integrated and do not provide very high-level parallel programming support. This research presents a number of issues which are considered to be key to the suitability of Java for HPC (High Performance Computing) applications and then explore the support for concurrency in the current Java 1.8 specification. We further present various relatively recent parallel Java models which support HPC for both shared and distributed memory programming paradigms. Finally, we attempt to evaluate the performance of discussed Java HPC models by comparing the same with the relative traditional native C implementations, where appropriate. The analysis of the results suggest that Java can achieve near similar performance to natively compiled languages, both for sequential and parallel applications, thus making it a viable alternative for HPC programming

A Virtualized SGE-based Computational Cluster for Heterogeneous Environments

The computing and modeling environment of IIASA was studied in the context of computation-intensive ad resource-demanding applications/models which are being developed and used by the researchers/scientists of IIASA. High Performance Computing applicatins can be classified into two broad computing fields; sequential distributed and parallel distributed applications and these applications has been developed for heterogeneous operating system architectures such as Linux, Windows and Solaris etc. Majority of IIASA applications/models belong to the latter class of computing and these applications are resource demanding when the extensive and repetitive use of these applications is required according to the need of some research study. Not every sequential application can be easily parallelized; therefore, instead of re-programming sequental applications into parallel ones, the idea of distributing such applications on computing cluster/grid is often an effective approach for accelerating the work. In the light of available computing resources and modest modeling environment of IIASA, the virtualization and Sun Grid Engine (batch job scheduler and manager for cluster/grid) was efficiently exploited and designed, built and tested. This resulted in a computational cluster supporting multiple operating systems and multiple sequential distributed and parallel distributed applications/models along with multiple job execution types such as binaries and JAVA

Java on Networks of Workstations (JavaNOW): A Parallel Computing Framework Inspired by Linda and the Message Passing Interface (MPI)

Networks of workstations are a dominant force in the distributed computing arena, due primarily to the excellent price/performance ratio of such systems when compared to traditionally massively parallel architectures. It is therefore critical to develop programming languages and environments that can help harness the raw computational power available on these systems. In this article, we present JavaNOW (Java on Networks of Workstations), a Java‐based framework for parallel programming on networks of workstations. It creates a virtual parallel machine similar to the MPI (Message Passing Interface) model, and provides distributed associative shared memory similar to the Linda memory model but with a richer set of primitive operations. JavaNOW provides a simple yet powerful framework for performing computation on networks of workstations. In addition to the Linda memory model, it provides for shared objects, implicit multithreading, implicit synchronization, object dataflow, and collective communications similar to those defined in MPI. JavaNOW is also a component of the Computational Neighborhood, a Java‐enabled suite of services for desktop computational sharing. The intent of JavaNOW is to present an environment for parallel computing that is both expressive and reliable and ultimately can deliver good to excellent performance. As JavaNOW is a work in progress, this article emphasizes the expressive potential of the JavaNOW environment and presents preliminary performance results only

Using Java for distributed computing in the Gaia satellite data processing

In recent years Java has matured to a stable easy-to-use language with the flexibility of an interpreter (for reflection etc.) but the performance and type checking of a compiled language. When we started using Java for astronomical applications around 1999 they were the first of their kind in astronomy. Now a great deal of astronomy software is written in Java as are many business applications. We discuss the current environment and trends concerning the language and present an actual example of scientific use of Java for high-performance distributed computing: ESA's mission Gaia. The Gaia scanning satellite will perform a galactic census of about 1000 million objects in our galaxy. The Gaia community has chosen to write its processing software in Java. We explore the manifold reasons for choosing Java for this large science collaboration. Gaia processing is numerically complex but highly distributable, some parts being embarrassingly parallel. We describe the Gaia processing architecture and its realisation in Java. We delve into the astrometric solution which is the most advanced and most complex part of the processing. The Gaia simulator is also written in Java and is the most mature code in the system. This has been successfully running since about 2005 on the supercomputer "Marenostrum" in Barcelona. We relate experiences of using Java on a large shared machine. Finally we discuss Java, including some of its problems, for scientific computing.Comment: Experimental Astronomy, August 201

NPB-MPJ: NAS Parallel Benchmarks Implementation for Message-Passing in Java

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/PDP.2009.59[Abstract] Java is a valuable and emerging alternative for the development of parallel applications, thanks to the availability of several Java message-passing libraries and its full multithreading support. The combination of both shared and distributed memory programming is an interesting option for parallel programming multi-core systems. However, the concerns about Java performance are hindering its adoption in this field, although it is difficult to evaluate accurately its performance due to the lack of standard benchmarks in Java. This paper presents NPB-MPJ, the first extensive implementation of the NAS Parallel Benchmarks (NPB), the standard parallel benchmark suite, for Message-Passing in Java (MPJ) libraries. Together with the design and implementation details of NPB-MPJ, this paper gathers several optimization techniques that can serve as a guide for the development of more efficient Java applications for High Performance Computing (HPC). NPB-MPJ has been used in the performance evaluation of Java against C/Fortran parallel libraries on two representative multi-core clusters. Thus, NPB-MPJ provides an up-to-date snapshot of MPJ performance, whose comparative analysis of current Java and native parallel solutions confirms that MPJ is an alternative for parallel programming multi-core systems.Ministerio de Educación y Ciencia; TIN2004-07797-C02Ministerio de Educación y Ciencia; TIN2007-67537-C03-02Xunta de Galicia; PGIDIT06PXIB105228P

Group Communication Patterns for High Performance Computing in Scala

We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable/Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 % efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000

A Distributed System for Parallel Simulations

We presented the technologies and algorithms to build a web-based visualization and steering system to monitor the dynamics of remote parallel simulations executed on a Linux Cluster. The polynomial time based algorithm to optimally utilize distributed computing resources over a network to achieve maximum frame-rate was also proposed. Keeping up with the advancements in modern web technologies, we have developed an Ajax-based web frontend which allows users to remotely access and control ongoing computations via a web browser facilitated by visual feedbacks in real-time. Experimental results are also given from sample runs mapped to distributed computing nodes and initiated by users at different geographical locations. Our preliminary results on frame-rates illustrated that system performance was affected by network conditions of the chosen mapping loop including available network bandwidth and computing capacities. The underlying programming framework of our system supports mixed-programming mode and is flexible to integrate most serial or parallel simulation code written in different programming languages such as Fortran, C and Java

Mixed language high-performance computing for plasma simulations

Java is receiving increasing attention as the most popular platform for distributed computing. However, programmers are still reluctant to embrace Java as a tool for writing scientific and engineering applications due to its still noticeable performance drawbacks compared with other programming languages such as Fortran or C. In this paper, we present a hybrid Java/Fortran implementation of a parallel particle-in-cell (PIC) algorithm for plasma simulations. In our approach, the time-consuming components of this application are designed and implemented as Fortran subroutines, while less calculation-intensive components usually involved in building the user interface are written in Java. The two types of software modules have been glued together using the Java native interface (JNI). Our mixed-language PIC code was tested and its performance compared with pure Java and Fortran versions of the same algorithm on a Sun E6500 SMP system and a Linux cluster of Pentium III machines

Design of efficient Java message-passing collectives on multi-core clusters

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-010-0464-5[Abstract] This paper presents a scalable and efficient Message-Passing in Java (MPJ) collective communication library for parallel computing on multi-core architectures. The continuous increase in the number of cores per processor underscores the need for scalable parallel solutions. Moreover, current system deployments are usually multi-core clusters, a hybrid shared/distributed memory architecture which increases the complexity of communication protocols. Here, Java represents an attractive choice for the development of communication middleware for these systems, as it provides built-in networking and multithreading support. As the gap between Java and compiled languages performance has been narrowing for the last years, Java is an emerging option for High Performance Computing (HPC). Our MPJ collective communication library increases Java HPC applications performance on multi-core clusters: (1) providing multi-core aware collective primitives; (2) implementing several algorithms (up to six) per collective operation, whereas publicly available MPJ libraries are usually restricted to one algorithm; (3) analyzing the efficiency of thread-based collective operations; (4) selecting at runtime the most efficient algorithm depending on the specific multi-core system architecture, and the number of cores and message length involved in the collective operation; (5) supporting the automatic performance tuning of the collectives depending on the system and communication parameters; and (6) allowing its integration in any MPJ implementation as it is based on MPJ point-to-point primitives. A performance evaluation on an InfiniBand and Gigabit Ethernet multi-core cluster has shown that the implemented collectives significantly outperform the original ones, as well as higher speedups when analyzing the impact of their use on collective communications intensive Java HPC applications. Finally, the presented library has been successfully integrated in MPJ Express (http://mpj-express.org), and will be distributed with the next release.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; FPU; AP2009-2112Xunta de Galicia; PGIDIT06PXIB105228P