GSoC 2023 Participants Announced!

It is time to announce the participants for in the STE||AR Group’s 2023 Google Summer of Code! We are very proud to announce the names of the 5 contributors this year who will be funded by Google to work on projects for our group.

These recipients represent the very best of the many excellent proposals that we had to choose from. For those unfamiliar with the program, the Google Summer of Code brings together ambitious students from around the world with open source developers by giving each mentoring organization funds to hire a set number of participants. Students then write proposals, which they submit to a mentoring organization, in hopes of having their work funded.

Below are the contributors who will be working with the STE||AR Group this summer listed with their mentors and their proposal abstracts.


Aarya Chaum, College of Engineering, Pune

Mentors: Rod Tohid, Shreyas Atre

Project: hpxMP: HPX threading system for LLVM OpenMP

One of the challenges in adopting HPX is the performance degradation observed in applications that use OpenMP. This occurs because of the contention between HPX threads and OpenMP’s native threading system (i.e., pthread) over available resources. hpxMP aims at resolving this issue by adding support for HPX threads as an alternative to pthreads in LLVM OpenMP. This work relies on the HPXC, which replicates pthread’s API.


Arnav Negi, International Institute of Information Technology, Hyderabad

Mentors: Shreyas Atre, Alireza Kheirkhahan

Project:  Async I/O using Coroutines and S/R – Traversing large scale graphs

If graphs are really large their adjacency lists become harder and slower to read and process. This can be a real concern in graph algorithms, as the I/O operations will slow them down considerably. The goal is to maximize speedup to this use case using asynchronous I/O and parallel algorithms. The implementation of this use case will use io_uring along with co-routines for asynchronously reading the graph files, senders and receivers to traverse the graph using the parallel execution policy par_unseq, and multiple NUMA domains to further accelerate memory access.


Hari Hara Naveen, Indian Institute of Technology

Mentors: Srinivas Singanaboina

Project: Add Vectorization to par_unseq Implementations of Parallel Algorithms

HPX parallel algorithms currently don’t support the par_unseq execution policy. This project is centered around the idea to implement this execution policy for at least some of the existing algorithms (such as for_each and similar).


Isidoros Tsaousis, Aristotle University of Thessaloniki

Mentors: Giannis Gonidelis

Project:  Implement hpx::relocate (P1144)

Modern C++ specifications and the HPX library offer a rich set of algorithms to ensure efficient resource utilization. Nevertheless, there is still room for improvement in data movement operations. Proposal P1144 introduces std::relocate, a feature designed to optimize data relocation by making it safer, faster, and greatly simpler. Essentially, std::relocate utilizes a single memcpy operation to move objects while avoiding unnecessary move-constructor and destructor calls. This improvement impacts key primitives like swap and vector.reserve, subsequently leading to speedup in higher-level algorithms such as rotate and sort. The goal of this proposal is to implement relocation in HPX.


Shubham Kumar, Indian Institute of Information Technology, Kalyan

Mentors: Steve Brandt, Rod Tohid

Project: Pythonize HPX!

the project aims to create a Python wrapper for the HPX task-based runtime system to make it more accessible to non-expert users who may not be proficient in C++. The HPX library provides parallel and distributed algorithms and data structures for C++, which can be challenging to use for beginners. The Python wrapper will address this challenge by providing a user-friendly interface for the HPX library, enabling users to leverage its power without requiring knowledge of C++. The project will help increase the accessibility of the HPX library and allow more people to benefit from its performance advantages. However, there are challenges associated with creating a Python binding for parallel computing, such as thread locking due to the Global Interpreter Lock (GIL), templates, reference counting, and handling. The deliverables of this project will include a Python wrapper for the HPX library, documentation, and examples to help users get started with the library.

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