Benchmarking User Level Threads

One of the core features of HPX is our lightweight user-level threading. User-level threading implements a second layer of thread infrastructure on top of OS-threads (e.g. thread implementations provided by the operating system or kernel). This form of threading is also called hybrid or M:N (mapping N user threads onto M OS-threads) threading.

We recently conducted a benchmark of the scalability of lightweight user-level threads in the face of extremely fine-grained parallelism. Fine-grained parallelism refers to the division of work into very small parallel tasks. By making the tasks very small, the task scheduler is able to load balance more efficiently in the face of highly dynamic applications.

This article presents details of the benchmark we used, and a comparison of HPX with three other software libraries which provide lightweight user-level threading (Qthreads, TBB and SWARM). Continue reading

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HPX V0.8.0 Released

We are very proud to announce the release of version 0.8.0 of our High Performance ParalleX (HPX) parallel runtime system. This is our third 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 previous release last November. We have had roughly 1000 commits since the last release and we closed approximately 70 tickets (bugs, feature requests, etc.). This post will expand on some of the most important changes we have made. Continue reading

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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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