The STE||AR Group (pronounced as stellar) stands for "Systems Technology, Emergent Parallelism, and Algorithm Research". We are an international group of faculty, researchers, and students working at different organizations. The goal of the STE||AR Group is to promote the development of scalable parallel applications by providing a community for ideas, a framework for collaboration, and a platform for communicating these concepts to the broader community.

All of our work is centered around building technologies for scalable parallel applications. HPX, our general purpose C++ runtime system for parallel and distributed applications, is no exeption. We use HPX for a broad range of scientific applications, helping scientists and developers to write code which scales better and shows better performance compared to more conventional programming models such as MPI.

HPX is based on ParalleX which is a new (and still experimental) parallel execution model aiming to overcome the limitations imposed by the current hardware and the way we write applications today. Our group focuses on two types of applications - those requiring excellent strong scaling, allowing for a dramatic reduction of execution time for fixed workloads and those needing highest level of sustained performance through massive parallelism. These applications are presently unable (through conventional practices) to effectively exploit a relatively small number of cores in a multi-core system. By extention, these application will not be able to exploit high-end computing systems which are likely to employ hundreds of millions of such cores by the end of this decade.

Critical bottlenecks to the effective use of new generation high performance computing (HPC) systems include:

The ParalleX model has been devised to address these challenges by enabling a new computing dynamic through the application of message-driven computation in a global address space context with lightweight synchronization. The work on hpx is centered around implementing the concepts as defined by the ParalleX model. HPX is currently targetted at conventional machines, such as classical Linux based Beowulf clusters and SMP nodes.

We fully understand that the success of HPX (and ParalleX) is very much the result of the work of many people. To see a list of who is contributing see our tables below.

HPX Contributors

Contributors to this Document

Acknowledgements

Thanks also to the following people who contributed directly or indirectly to the project through discussions, pull requests, documentation patches, etc.

In addition to the people who worked directly with HPX development we would like to acknowledge the NSF, DoE, DARPA, Center for Computation and Technology (CCT), and Department of Computer Science 3 - Computer Architecture who fund and support our work. We would also like to thank the following organizations for granting us allocatons of thier compute resources: LSU HPC, LONI, XSEDE and the Gauss Center for Supercomputing.

HPX is currently funded by:

The National Science Foundation through awards 1117470 (APX) and 1240655 (STAR). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

The Department of Energy (DoE) through the award DE-SC0008714 (XPRESS). Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

The Bavarian Research Foundation (Bayerische Forschungsstfitung) through the grant AZ-987-11.