We are pleased to announce the 1^st place Best Poster Award winner, Maxwell Cole, for his poster: Computational feasibility of simulating radiation induced changes to vasculature and blood flow rates in the entire human body.

2^nd place was awarded to Ioannis Gonidelis for the poster titled Evaluating and Improving Shared Memory Performance of HPX and OpenMP using Task Bench.

Each year, the Best Poster Award recognizes outstanding presentations in the conference’s Poster Session. Posters are judged by external workshop attendees.

We like to thank HPE Enteprise for sponsoring the poster prices.

Maxwell’s poster can be viewed at the following link: https://zenodo.org/record/7647521#.Y_fLWS-B1KM

Kishore Kumar, International Institute of Information Technology, Hyderabad

Adapting std Algorithms for the unseq and par_unseq Execution Policies

I began my work by first analyzing and testing compiler support and codegen for different user provided hints. This was used to create the original version of #6016. Later, I added support for the omp backend which is supported by later versions of Clang and ICC out of the box. As of the latest PR the unseq backend will first attempt to use the omp backend, and if it is not available, default to compiler specific hints. 

After this, the next task assigned to me was to implement a basic version of the transform_loop and loop CPO’s. This was initially completed keeping in mind just supporting the original non-omp backend. Later, it was ported to account for supporting the omp backend as well. In particular, GCC will throw errors if the loops asked to vectorize are not conforming to the standard syntax:

for(int counter=0; counter < limit; counter++) { … }

So the implementation was then changed to vectorize loops only when passed std::random_access_iterator’s. This is #6017.

Following this, I wrote a mini-benchmark environment for testing the performance of my adaptation of the std algorithms here. This exists as a separate repo and was used to report all the benchmark numbers shown here. 

A strong case for switching to the omp backend was its support for declaring reductions on supported clauses. The next task I worked on was implementing an efficient version of the reduce CPO’s here #6018. Reductions for default-supported ones were overloaded to their respective methods, and a generalized implementation is given as well. This mostly gets the job done, however for the specialized-overloads to accept the overload the reduction operation must exactly match the type of the init value. For example, if reduction is over unsigned int and init is signed, the overload will not accept. This is a TODO that I believe is possible to achieve with more template meta-programming. I will be working on this post GSoC.

Note: GCC Unseq can probably be made a decent amount faster by switching to the omp backend (Does not default support). Also, clang no-vec benchmarks were removed from the chart as they were very slow and skewed the visualization. 

Please find attached the final reports of 3 of our successful GSoC students:

Panagiotis Syskakis, HPX Algorithm Performance Analysis & Optimization: Blog/Final Report

Monalisha Ojha, Multiple Datasets Performance Visualization: Blog/Final Report

Shreyas Atre, Coroutines and HPX: Final Report , Slides

In the second week of July, we completed the first evaluation of our Google Summer of Code program. The students have provided summaries of their work and details of the pull requests they’ve created. Check them out below:

Monalisha Ojha:

https://medium.com/@monalisha-ojha/multiple-datasets-performance-visualization-traveler-a352c13f7c25

Multiple Datasets Performance Visualization — Traveler

Phase-1 of Google Summer of Code 2022 at Stellar Group

This summer, I am working as a Google Summer of Code mentee in STE||AR Group on “Upgrading Multiple Datasets Performance Visualization feature in Traveler” under the mentorship of Kate Isaacs. This blog summarizes my work on the Traveler Platform during phase 1 of Google Summer of Code 2022 program.

About Traveler

Traveler-Integrated is a web-based visualization system for parallel performance data, such as OTF2 traces and HPX execution trees. HPX traces are collected with APEX and written as OTF2 files with extensions. It is developed by the HDC Lab (Humans, Data and Computers Lab) at the University of Arizona.The major goal of this platform is to provide meaningful insights into parallel performance data in the form of Gantt charts (trace data timelines with dependencies), source code, expression tree, aggregated time series line charts for counter data, utilization chart and task level histograms.

Abstract

The aim of this project, “Multiple Datasets Performance Visualization,’’ is to add specific features in the platform that will help in managing multiple data files and organizing traveler interface windows to handle the comparison of data. Organizing multiple datasets in the platform, comparison of datasets side by side, implementing a highlighted linking system for multiple datasets and organizing datasets efficiently for visualization are some of the major sub-goals.

Phase — 1

Updated the Tagging system of Traveler Interface to accommodate multiple datasets

Issue : Organizing the datasets according to their assigned tags.

Made changes in the interface main menu to display the datasets according to their tags names. Tested the tagging system back-end to accommodate multiple datasets. The screenshot displays the fixes made when tested with 2 datasets.

Issue Link: https://github.com/hdc-arizona/traveler-integrated/issues/90

Pull Request: https://github.com/hdc-arizona/traveler-integrated/pull/91

Fixed glitches related Traveler front-end

Issue: Displaying a clear relationship between a folder and its datasets.

Made changes in the front-end to make the lines visible that shows the connection between folder and its datasets. Adjusted the tag header to solve the tag overlapping issue for multiple datasets. The screenshot of the changes are shown below.

Issue link: https://github.com/hdc-arizona/traveler-integrated/issues/92

Pull request link: https://github.com/hdc-arizona/traveler-integrated/pull/93

Adding dynamic color highlighting system

Issue: Adding a color picker system to distinguish between multiple datasets.

“Change Datasets color” option is added to datasets context menu. With this feature, a user can change the datasets selection color and main menu color to be distinguishable from other datasets. The screenshots of changes done till now are displayed below:

Pull request link: https://github.com/hdc-arizona/traveler-integrated/pull/94

Shreyas Atre

https://satacker.github.io/docs/c++/GSoC-HPX/

• GSoC Project Acceptance Page
• Project Title: Coroutine-like interface

Mentors (STE||AR Group @ LSU)

1. Dr. Hartmut Kaiser, Adjunct Professor @ LSU
2. Giannis Gonidelis, RA @ LSU

Abstract#

HPX being up to date with Std C++ Proposals, Senders/Receivers were implemented as per P2300. But they have been missing coroutine (co_await) integration and minor functionalities as described in P2300 which is likely to be accepted. Hence I plan to implement these functionalities within the Core HPX Library.

• Benefits:
  • Coroutines introduce better async code. For example, it is more readable, local variables have the same lifespan as the coroutine which means we don’t need to worry about allocation/release.
  • S/R algorithms can work with coroutines which they cannot as of now unless relied on futures which as mentioned are single-time use.
  • Adding co_await support makes the code more structured with respect to concurrency which can also be done by library abstractions of callbacks but using co_await may make it more optimized.

Brief Summary#

• Senders, and Receivers
  • Because it makes a more consistent programming model considering async programming types i.e. Parallelism and Concurrency. It standardizes the terminologies and execution policies which are more generic and reduce redundancy.
  • Coroutines have a direct connection between Senders and Coroutine Awaitables.
• Futures
  • One of the points of S/R is to avoid the allocations associated with futures, also, futures are single-use, whereas S/R, in general, can be used (started) multiple times. – Dr. H. Kaiser

Goal is to enable all Sender CPOs to do the following:

• If we write a sender and pass it to a function which could be a coroutine that could co_await that sender and get its result.
• If they are not generally awaitable then we can await transform them (i.e. make them awaitable).

Work#

My PRs can be found using this link as it’ll always be updated.

Following are the Merged PRs until now:

• [execution] Add schedule_result_t alias template
• [hpx::execution] Added forwarding_scheduler_query
• [P2300] enhancements: receiver_of, sender_of improvements
• [P2300] Added fundamental coroutine_traits for S/R

With coroutine traits completed, my remaining work is the following:

1. Adapt get_completion_signatures when Sender is a awaitable
2. Utility as_awaitable_t
   • receiver_base, sender_awaitable_base
   • to transform an object into one that is awaitable within a particular coroutine.
3. promise base for 5.
4. operation base for 5.
5. Utility connect_awaitable to adapt connect mentioned in spec 2.2
6. Utility with_awaitable_senders
   • Used as the base class of a coroutine promise type, makes senders awaitable in that coroutine type

References#

• P2300

Panagiotis Syskakis:

I’m Panos, currently studying Electrical and Computer Engineering in Aristotle University of Thessaloniki, in Greece. This summer, I joined the HPX team as a contributor through Google Summer of Code (GSoC).

My GSoC project involves performance analysis and optimization on C++ standard parallel algorithms.

To explain further:
The C++ standard defines many functions for algorithms that are commonly used by developers (eg. sorting, searching).
HPX provides sequential and parallel implementations for all these algorithms.
I’m working on improving the performance of these implementations.

So far, I have explored different methodologies for visualizing and assessing an algorithm’s performance. This has involved a lot of scripting for automating tasks, as well as data collection and analysis.

With help from my mentor, I have produced plots that show how an algorithm’s performance changes when tweaking different parameters (such as workload size and number of computer cores). We also produced visualizations of how different tasks are distributed and where/how they are executed in a parallel environment.

Most importantly though:
The HPX community has been immensely welcoming. It can often be awkward being “the new junior guy”, but my mentor quickly made me feel like a part of the team.
People here are talented, but also fun and humble, and always eager to help.

This summarizes my experience for the first two months of GSoC. I have learned tons so far. My work here is far from done, however we have laid a great foundation for the work that will follow.