Is ParalleX This Year’s Model?

This is an interview John Moore held with Hartmut Kaiser, the lead of the STE||AR group at CCT. Source: Intelligence in Software.

Scientific application developers have masses of computing power at their disposal with today’s crop of high-end machines and clusters. The trick, however, is harnessing that power effectively. Earlier this year, Louisiana State University’s Center for Computation & Technology (CCT) released its approach to the problem: an open-source runtime system implementation of the ParalleX execution model. ParalleX aims to replace, at least for some types of applications, the Communicating Sequential Processes (CSP) model and the well-established Message Passing Interface (MPI), a programming model for high-performance computing. The runtime system, dubbed High Performance ParalleX (HPX) is a library of C++ functions that targets parallel computing architectures. Hartmut Kaiser — lead of CCT’s Systems Technology, Emergent Parallelism, and Algorithm Research (STE||AR) group and adjunct associate research professor of the Department of Computer Science at LSU — recently discussed ParalleX with Intelligence in Software. Continue reading

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Solving the n-body Problem Using HPX

The n-body problem, i.e. the prediction of the motion of a group of objects that interact with each other under the influence of a force, is a method that continues to present a computational challenge to scientist in a broad range of application areas, like astrophysics or computational biology. Existing codes are usually scaling-challenged causing overly long runtimes for real-world problem sizes. We hope to overcome some of the challenges that face computing an n-body problem by using a message driven, inherently asynchronous approach based on the HPX (High-Performance ParalleX) library.  Continue reading

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HPX 0.7.0 Released

We are very proud to announce the release of version 0.7.0 of our High Performance ParalleX (HPX) runtime system. This is our second formal release, and we would like to thank everyone involved for their hard work which has made this release possible. You can download the release files from the downloads page. The release note are available from here. Please feel free to try the examples and let us know what you think. The best way to get in contact with us is to leave a comment on this page or to send a mail to gopx@cct.lsu.edu.

We have made substantial progress since the 0.6.0 release last August. We have had roughly 1000 commits since the last release, closed approximately 120 tickets (bugs, feature requests, etc.).

This post will expand on three of the most important advances that we have made since the last release.

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STE||AR at Supercomputing 2011

We already wrote about our demos we will show at Supercomputing 2011 next week here. Please come visit us at the LSU booth – 2839. The week at the Supercomputing conference will be an exciting week for our group as it is the first time we present our work to the public. We will demonstrate the HPX (High Performance ParalleX) Runtime System technology which is providing the first freely available, open source implementation of the ParalleX execution model. HPX is solidly based on many years of experience in writing highly parallel applications for HPC systems. It is a modular, feature-complete, and performance oriented representation of the ParalleX execution model targeted at conventional architectures and, currently, Linux based systems, such as SMP nodes and conventional clusters. Continue reading

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STE||AR–A New Name for Our Group

STE||AR (pronounced as stellar) stands for “Systems Technologies, Emergent Parallelism, and Algorithms Research”. We decided to use this name for our group because the focus of our work has shifted over the last months. We are a group of faculty, researchers and students working at the Center of Computation and Technology (CCT) at Louisiana State University (LSU). Everything we do is centered around the ParalleX execution model and its implementation in our experimental runtime system HPX (High Performance ParalleX). We use HPX for a broad range of scientific applications, helping scientists and developers to write code which scales better and shows better performance if compared to more conventional programming models such as MPI. Continue reading

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