Shaping a Scalable Future

Today’s application developers are faced with the harsh reality that the conventional methods of parallel execution are up against the wall. Hardware development is beginning to slow, and the free lunch is almost over. In this climate, it is more important now than ever to rethink our traditional approaches to parallel computation. New solutions must be developed with the scalability of applications being central to their design.

The STE||AR Group is an international team of researchers who understand that a new approach to parallel computation is needed. Our work is crafted around the idea that we need to invent new ways to more efficiently use the resources that we have and use the knowledge that we gain to help guide the creation of the machines of tomorrow. While we develop several software products, the library which is most heavily developed and core to our team is HPX. The HPX runtime system is stirring up the pot by providing a modern implementation of all C++ standard facilities related to parallelism and concurrency, extended for the distributed use case. HPX is a general purpose C++ runtime system for parallel and distributed applications of any scale. It strives to provide a unified programming model which transparently utilizes the available resources to achieve unprecedented levels of scalability. For more information about HPX please see here. As you can see from our ideas page, there are more possible projects for yourself to be involved with -- be creative!

We envision HPX as a library which provides services to applications which makes writing efficient, maintainable, and scalable parallel and distributed codes much simpler than current popular paradigms. Currently, HPX is used to develop scientific and industrial applications and in the future we hope to expand its influence to include common applications that touch our everyday lives.

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Technologies

  • c++
  • python
  • boost

Topics

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STE||AR Group 2021 Projects

  • Akhil J Nair
    Adapting algorithms to C++ 20 and Ranges TS
    Adapt the remaining algorithms like unique, adjacent_difference, lexicographical_compare etc. to C++ 20 and to Ranges TS.
  • Srinivas Yadav
    Add vectorization to par_unseq implementations of Parallel Algorithms
    The current HPX Parallel Algorithms do no support vectorization. This project aims at providing vectorization support to Parallel Algorithms with...
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2021