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(Redirected from High Performance Parallex)
Not to be confused with Hemopexin, a glycoprotein encoded by the HPX gene.
HPX
Developer(s)The STEllAR Group, archived from the original on 2019-04-03, retrieved 2019-04-03
LSU Center for Computation and Technology
Initial release2008 (2008)
Stable release
1.10.0 / May 29, 2024; 6 months ago (2024-05-29)
Repositorygithub.com/STEllAR-GROUP/hpx
Written inC++
Operating systemMicrosoft Windows
Linux
Mac OS X
TypePartitioned global address space
Parallel programming
Runtime System
LicenseBoost Software License[1]
Websitehpx.stellar-group.org

HPX, short for High Performance ParalleX, is a runtime system for high-performance computing. It is currently under active development by the STE||AR group[2] at Louisiana State University. Focused on scientific computing, it provides an alternative execution model to conventional approaches such as MPI. HPX aims to overcome the challenges MPI faces with increasing large supercomputers by using asynchronous communication between nodes and lightweight control objects instead of global barriers, allowing application developers to exploit fine-grained parallelism.[3][4][5]

HPX is developed in idiomatic C++ and released as open source under the Boost Software License, which allows usage in commercial applications.

Applications

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Though designed as a general-purpose environment for high-performance computing, HPX has primarily been used in

References

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  1. ^ "License", Boost Software License – Version 1.0, boost.org, retrieved 2012-07-30
  2. ^ "About the STE||AR Group". Retrieved 17 April 2019.
  3. ^ Kaiser, Hartmut; Brodowicz, Maciek; Sterling, Thomas (2009). "ParalleX an Advanced Parallel Execution Model for Scaling-Impaired Applications". 2009 International Conference on Parallel Processing Workshops. pp. 394–401. doi:10.1109/icppw.2009.14. ISBN 978-1-4244-4923-1. S2CID 898158.
  4. ^ Wagle, Bibek; Kellar, Samuel; Serio, Adrian; Kaiser, Hartmut (2018). "Methodology for Adaptive Active Message Coalescing in Task Based Runtime Systems". 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 1133–1140. doi:10.1109/IPDPSW.2018.00173. ISBN 978-1-5386-5555-9. S2CID 51921994.
  5. ^ a b Wagle, Bibek; Monil, Mohammad Alaul Haque; Huck, Kevin; Malony, Allen D.; Serio, Adrian; Kaiser, Hartmut (2019). "Runtime Adaptive Task Inlining on Asynchronous Multitasking Runtime Systems". Proceedings of the 48th International Conference on Parallel Processing. pp. 1–10. doi:10.1145/3337821.3337915. ISBN 9781450362955. S2CID 198963569.
  6. ^ C. Dekate, M. Anderson, M. Brodowicz, H. Kaiser, B. Adelstein-Lelbach and T. Sterling (2012). "Improving the Scalability of Parallel N-body Applications with an Event-driven Constraint-based Execution Model". International Journal of High Performance Computing Applications. 26 (3): 319–332. arXiv:1109.5190. doi:10.1177/1094342012440585. S2CID 9556798.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ M. Anderson, T. Sterling, H. Kaiser and D. Neilsen (2011). "Neutron Star Evolutions using Tabulated Equations of State with a New Execution Model" (PDF). American Physical Society April 2012 Meeting.{{cite web}}: CS1 maint: multiple names: authors list (link)
  8. ^ D. Pfander, G. Daiß, D. Marcello, H. Kaiser, D. Pflüger, David (2018). "Accelerating Octo-Tiger: Stellar Mergers on Intel Knights Landing with HPX". DHPCC++ Conference 2018 Hosted by IWOCL. doi:10.1145/3204919.3204938. S2CID 21126354.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  9. ^ Marcello, Dominic; Daiß, Gregor; Parsa Amini; Kaiser, Hartmut; Diehl, Patrick; Wash, Bryce Adelstein Lelbach Aka; Heller, Thomas; Shibersag; Huck, Kevin; Biddiscombe, John; Schäfer, Andreas (2019-04-17), STEllAR-GROUP/octotiger Repository on GitHub, The STE||AR Group, doi:10.5281/zenodo.5093174, retrieved 2019-04-17
  10. ^ Heller, Thomas; Lelbach, Bryce Adelstein; Huck, Kevin A; Biddiscombe, John; Grubel, Patricia; Koniges, Alice E; Kretz, Matthias; Marcello, Dominic; Pfander, David (2019-02-14). "Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars". The International Journal of High Performance Computing Applications. 33 (4): 699–715. doi:10.1177/1094342018819744. ISSN 1094-3420. OSTI 1524389.
  11. ^ "LibGeoDecomp – Petascale Computer Simulations". www.libgeodecomp.org. Archived from the original on 2022-06-25. Retrieved 2019-04-17.
  12. ^ A library for C++/Fortran computer simulations (e.g. stencil codes, mesh-free, unstructured grids, n-body & particle methods). Scales from smartphones to petascale supercomputers (e.g. Titan, T.., The STE||AR Group, 2019-04-06, retrieved 2019-04-17
  13. ^ A. Schäfer, D. Fey (2008). "LibGeoDecomp: A Grid-Enabled Library for Geometric Decomposition Codes". Recent Advances in Parallel Virtual Machine and Message Passing Interface. Lecture Notes in Computer Science. Vol. 5205. pp. 285–294. doi:10.1007/978-3-540-87475-1_39. ISBN 978-3-540-87474-4.
  14. ^ Diehl, Patrick; Jha, Prashant K.; Kaiser, Hartmut; Lipton, Robert; Levesque, Martin (2020). "An asynchronous and task-based implementation of peridynamics utilizing HPX—the C++ standard library for parallelism and concurrency". SN Applied Sciences. 2 (12). arXiv:1806.06917. doi:10.1007/s42452-020-03784-x. S2CID 227240479.
  15. ^ "Phylanx – A Distributed Array Toolkit". Retrieved 2019-04-17.
  16. ^ An Asynchronous Distributed C++ Array Processing Toolkit: STEllAR-GROUP/phylanx, The STE||AR Group, 2019-04-16, retrieved 2019-04-17
  17. ^ Tohid, R.; Wagle, Bibek; Shirzad, Shahrzad; Diehl, Patrick; Serio, Adrian; Kheirkhahan, Alireza; Amini, Parsa; Williams, Katy; Isaacs, Kate; Huck, Kevin; Brandt, Steven; Kaiser, Hartmut (2018). "Asynchronous Execution of Python Code on Task-Based Runtime Systems". 2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2). pp. 37–45. arXiv:1810.07591. doi:10.1109/ESPM2.2018.00009. ISBN 978-1-72810-178-1. S2CID 52988499.
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