Java ProActive vs. Fortran MPI: Looking at the future of parallel Java

Amedro, Brian; Caromel, Denis; Huet, Fabrice; Bodnartchouk, Vladimir

doi:10.1109/ipdps.2008.4536337

Cited by 3 publications

(1 citation statement)

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“…The poor performance of the language in its early days -mainly caused by slow bytecode interpretation and garbage collection pauses -hindered its adoption, along with some numerical issues that are not completely solved yet [92,35]. Nevertheless, the continuous improvements in the Just-in-Time compilers of JVMs, which transform bytecode to native code at execution time, have significantly narrowed the gap between Java's performance and that of languages like C/C++ [165,82,162,1]. Java is extensively used in distributed computing, primarily in software for the Web [8,32] but also in frameworks for big-data applications [6] and distributed databases [4], or even in contest-winning systems [28].…”

Section: Starss For Parallel and Distributed Infrastructuresmentioning

confidence: 99%

Programming and parallelising applications for distributed infrastructures

Tejedor Saavedra

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The last decade has witnessed unprecedented changes in parallel and distributed infrastructures. Due to the diminished gains in processor performance from increasing clock frequency, manufacturers have moved from uniprocessor architectures to multicores; as a result, clusters of computers have incorporated such new CPU designs. Furthermore, the ever-growing need of scienti c applications for computing and storage capabilities has motivated the appearance of grids: geographically-distributed, multi-domain infrastructures based on sharing of resources to accomplish large and complex tasks. More recently, clouds have emerged by combining virtualisation technologies, service-orientation and business models to deliver IT resources on demand over the Internet. The size and complexity of these new infrastructures poses a challenge for programmers to exploit them. On the one hand, some of the di culties are inherent to concurrent and distributed programming themselves, e.g. dealing with thread creation and synchronisation, messaging, data partitioning and transfer, etc. On the other hand, other issues are related to the singularities of each scenario, like the heterogeneity of Grid middleware and resources or the risk of vendor lock-in when writing an application for a particular Cloud provider. In the face of such a challenge, programming productivity - understood as a tradeo between programmability and performance - has become crucial for software developers. There is a strong need for high-productivity programming models and languages, which should provide simple means for writing parallel and distributed applications that can run on current infrastructures without sacri cing performance. In that sense, this thesis contributes with Java StarSs, a programming model and runtime system for developing and parallelising Java applications on distributed infrastructures. The model has two key features: first, the user programs in a fully-sequential standard-Java fashion - no parallel construct, API call or pragma must be included in the application code; second, it is completely infrastructure-unaware, i.e. programs do not contain any details about deployment or resource management, so that the same application can run in di erent infrastructures with no changes. The only requirement for the user is to select the application tasks, which are the model's unit of parallelism. Tasks can be either regular Java methods or web service operations, and they can handle any data type supported by the Java language, namely les, objects, arrays and primitives. For the sake of simplicity of the model, Java StarSs shifts the burden of parallelisation from the programmer to the runtime system. The runtime is responsible from modifying the original application to make it create asynchronous tasks and synchronise data accesses from the main program. Moreover, the implicit inter-task concurrency is automatically found as the application executes, thanks to a data dependency detection mechanism that integrates all the Java data types. This thesis provides a fairly comprehensive evaluation of Java StarSs on three di erent distributed scenarios: Grid, Cluster and Cloud. For each of them, a runtime system was designed and implemented to exploit their particular characteristics as well as to address their issues, while keeping the infrastructure unawareness of the programming model. The evaluation compares Java StarSs against state-of-the-art solutions, both in terms of programmability and performance, and demonstrates how the model can bring remarkable productivity to programmers of parallel distributed applications.

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Section: Starss For Parallel and Distributed Infrastructuresmentioning

confidence: 99%