GSoC 2022 Participants Announced!

It is time to announce the participants for in the STE||AR Group’s 2022 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.


Participant:

Shreyas Swanand Atre, Veermata Jijabai Technological Institute

Mentors:

Giannis Gonidelis

Hartmut Kaiser

Project: Coroutine-like interface

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 propose 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.


Participant:

Panos Syskakis, Aristotle University of Thessaloniki

Mentors:

Giannis Gonidelis

Hartmut Kaiser

Project:  HPX Algorithm Performance Analysis & Optimization

The latest C++ specifications and the HPX library introduce a variety of ready-to-use algorithms that may use parallelization and concurrency, in order to more efficiently utilize system resources. However, current implementations of parallel algorithms don’t always perform ideally (low thread utilization, large overhead, in some cases slower than sequential). The goal of this project is to investigate this under-performance and improve current implementations, using scaling analysis, profiling tools and visualizations.


Participant:

Bo Chen, University of Science and Technology Beijing

Mentors:

Patrick Diehl

Project: Implement your favorite Computational Algorithm in HPX ( Molecular Dynamics Simulation of Metal)

My Implement will base on MISA-MD. There are various potential functions used in MD simulation under fields, such as Tersoff potential and Lennard-Jones (L-J) potential, for calculating the interaction among atoms. To improve the simulation accuracy, MISA-MD adopted Embedded Atom Method (EAM) potential, a complex but pretty accurate potential Function, which can provide an effective interatomic description for metallic system. To improve the runtime performance, MISA-MD designed and realized a new hash based data structure for efficient atom storage and quick neighbor atom indexing.


Participant:

Kishore Kumar, International Institute of Information Technology, Hyderabad

Mentors:

Nikunj Gupta

Srinivas Yadav

Project:  Implementing auto-vectorization hints for par_unseq and unseq versions of HPX parallel algorithms

C++ 17 and 20 released the par_unseq and unseq execution models which give guarantees to functions which specialize on them that data access functions can be interleaved even between iterations of one thread. This means that these functions are vectorization safe and can thus gain massive boosts in performance by compiler auto-vectorization. Compilers however are conservative and auto-vectorize loops only when they are sure that vectorized versions give the same result as their scalar counterparts and that vectorization will actually end up being profitable. GCC, Clang, MSVC, ICC all rely on different optimization passes in their backend and are all capable of auto-vectorizing certain loop patterns but not all. The goal of this project is to analyze compiler codegen response to different hints and implement a version of the par_unseq and unseq execution policies in HPX that makes use of these guarantees to provide compilers with as many hints as possible to encourage auto-vectorization.


Participant:

Monalisha Ojha, Birla Institute of Technology, Mesra

Mentors:

Kate Isaacs

Project: Multiple Dataset Performance Visualization

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

GSoD 2022

STE||AR Group was accepted for Google Season of Docs 2022! We look forward to developing our HPX documentation even more and expanding our group this summer.

https://developers.google.com/season-of-docs/docs/participants

Like Google Summer of Code (GSoC) the program aims to match motivated people with interesting open source projects that are looking for volunteer contributions. GSoD, however, aims to improve open source project documentation, which often tends to get less attention than the code itself.

We are now looking for motivated people to help us improve our documentation. If you have some prior experience with technical writing, and are interested in working together with us on making the documentation of a cutting edge open source C++ library the best possible guide for new and experienced users, this is your chance. You can read more about the program on the official GSoD home page. We’ve provided a few project ideas on our wiki, but you can also come up with your own. Our current documentation can be found here.

GSoC 22: Come and code a STE||AR Summer with us!

The STE||AR Group is honored to be selected as one of the 2022 Google Summer of Code (GSoC) mentor organizations! This program, which pays students over the summer to work on open source projects, has been a wonderful experience for students and mentors alike. This is our 8th summer being accepted by the program!

Interested students can find out more about the details of the program on GSoC’s official website. As a mentor organization we have come up with a list of suggested topics for students to work on, however, any student can write a proposal about any topic they are interested. We find that students who engage with us on IRC (#ste||ar on freenode) or via our mailing list hpx-users@stellar-group.org have a better chance of having their proposals accepted and a better understanding of their project scope. Students may also read through our hints for successful proposals.

If you are interested in working with an international team of developers on the cutting edge of C++ parallel, task-based runtime systems please check us out!

STE||AR Spotlight: Srinivas Yadav

Srinivas Yadav is a final year under-graduate student pursuing a Bachelors in Computer Science in India. He has been working for STE||AR Group for 8 months now and is interested in the area of vectorization in the field of HPC. He has worked on HPX for the project “Adding par_simd implementations to parallel algorithms”.

Srinivas relied on guidance from STE||AR Group members, Prof. Hartmut Kaiser, Nikunj Gupta, and Auriane Reverdell, to work through his GSoC project.

Srinivas published and presented the paper “Parallel SIMD – A Policy Based Solution for Free Speed-Up using C++ Data-Parallel Types at ESPM2, SC21 in November 2021.

While working with STE||AR Group and HPX Srinivas has learned more about the field of computer science and programming. He learned how to use HPC clusters, and was given remote access to Rostam Cluster at LSU, CCT by Hartmut Kaiser to run SIMD benchmarks on different machines available. Currently, Srinivas is working remotely on Ookami Cluster at Stony Brook University to perform the SIMD benchmarks for ARM machines (on A64FX node) and working with octo-tiger to  port new vectorization backends which could be used to run the octo-tiger benchmarks on these nodes

He also learned the importance of collaboration with different researchers from different time zones. Getting international schedules together can be tricky!

Currently, Srinivas is working on three key areas

First is adapting algorithms to SIMD policies in HPX. The main aim of this task is to adapt as many algorithms as possible to SIMD execution policies in HPX, which contributes to fixing #2333.

Second is to port std::experimental::simd to Octo-Tiger. There are many kernels in Octo-Tiger library which currently use HPX-Kokkos with Vc library for vectorization, but now Vc is deprecated and the plan isto replace it with std::experimental::simd.

Finally, porting the EVE library as a new vectorization/simd backend to HPX. HPX currently has two vectorization backends, a newer one is std::experimental::simd, the older one is Vc (now deprecated) so needs to be replaced by a newer library and EVE seems to be perfect fit for that slot.

Srinivas has applied to LSU for a masters in Computer Science for the coming Fall Semester 2022.  He is very excited to come to CCT and LSU!

Other than academics/work, Srinivas enjoys playing badminton, watching and playing cricket and exploring new places/traveling a lot. Recently, he started learning cooking and learning a new Indian language (Tamil).

STE||AR Spotlight: Alireza Kheirkhahan

Alireza Kheirkhahan is an IT Consultant in the STE||AR Group at LSU.  He received his B.S in Computer Engineering from Sharif University of Technology, Tehran, Iran and his master’s in computer science from LSU.

Alireza’s master thesis focused on I/O backend and Storage solutions. His main research focus is High Performance Computing, I/O Systems, high-throughput, redundant and distributed storage systems

Alireza designed and implemented STE||AR Group at LSU’s small research cluster, Rostam. Since 2015, he manages and improves this cluster. Currently, the second generation of the computer cluster is in use, and the next generation is in design. Rostam consists of nearly 80 compute nodes and multiple storage servers. Over the course of the years, more than a dozen graduate students used Rostam for their thesis work, and more than thirty scientific publications have been created using this cluster.

On the CERA (Coastal Emergency Risk Assessment) project, Alireza acts as residing high performance specialist. He carries the specific hpc tasks for domain scientists.  He adapts and maintains their computational needs in HPC clusters provided by LSU and the State of Louisiana. 

Alireza designed and implemented a special purpose storage system for CERA projects. CERA has a particular need for a specific storage solution. The application creates bursts of gigabytes of data at once and goes quite for few hours until another burst arrives. The recently generated data is highly valued and must have very quick and reliable access, but the older data must be archived with a cost-effective manner. With his research background, Alireza designed the new storage system to carry out both tasks at once, which reduced the data transfer time significantly and increased reliability.

Alireza lives in Baton Rouge, with his wife Shahrzad and son Damon. In his spare time, he enjoys woodworking and tinkering with electronics. 

STE||AR Student Wins University Research Award!

Nikunj Gupta, a STE||AR Group GSoC student from 2018 and continued collaborator with our group, has received a Thesis Award from his University, IIT Roorkee, based on his research. 

IIT Roorkee allows students to pursue research in foreign universities (Foreign BTP), wherein the student should have 1 supervisor (IIT Roorkee Prof) and 1 co-supervisor, the professor under which the student pursues research. Dr. Hartmut Kaiser introduced Nikunj to Prof. Sanjay Laxmikant V. Kale, at the University of Illinois at Urbana-Champaign for the purposes of this project. Dr. Kale interviewed Nikunj and agreed to provide a research internship from Sept-Dec. Nikunj proposed this research as Foreign BTP to his University and was accepted. 

After completion of his research internship, Nikunj received an award for his project during his University’s Convocation Ceremony held on Sept 11, 2021.

The details of the project, provided by Nikunj, and are below.  Congratulations, Nikunj!  We are proud of the work you do.

Sept-Dec:

I worked with Sanjay on part 1 of my thesis, which was to write abstractions over Charm++ to generate a linear algebra library. The salient features of this library were out-of-order execution, completely asynchronous operations and almost linear distributed scaling. As a future work, I decided to optimize the library (something I’m doing right now as a graduate student here at UIUC) and to generate a frontend language that has a MATLAB like syntax.

Jan-June:

I felt that I could add more to my thesis to make it stronger, so I decided to add my resilience work to it. I contacted Hartmut regarding resiliency work to which he agreed. The work was to implement resilience execution spaces in Kokkos. I created Kokkos executors for HPX to use Kokkos facilities to achieve resilience within HPX and also added resilience execution spaces in Kokkos to achieve resilience within Kokkos. The resilience scheme within Kokkos is an auto-generative scheme that will work with all current and future execution spaces. I also included my past work with resilience in my thesis. So, the work I did back in Summer 2019 and Summer 2020 was included in my thesis too. In Summer 2019, I worked on implementing Local-Only Software Resilience in HPX which I extended to Distributed Software Resilience in Summer 2020. The resilience work has also been published last year at the FTXS workshop in Supercomputing titled “Towards distributed software resilience in asynchronous many-task programming models”. We plan on writing a paper on the Kokkos resilient execution space as well!

STE||AR Spotlight: Nanmiao Wu

Nanmiao Wu is a Ph.D. student In the Department of Electrical and Computer Engineering and Center for Computation and Technology, LSU. She has been working in STE||AR group for more than 2 years and is co-advised by Dr. Hartmut Kaiser, head of the STE||AR Group, and Dr. Ram Ramanujam, Director of CCT. 

Before joining LSU, she received a B.S. degree in Electronic Information Science and Technology from Nankai University, and an M.S. degree in Electrical and Computer Engineering from the University of Macau.

Nanmiao’s research focuses on scalable and distributed high-performance computation for machine learning and deep learning applications.

She has been an intern at Pacific Northwest National Laboratory (PNNL) from February to August  2021, developing a HPX runtime interface for a C++ algorithm and data-structure library, SHAD, for better scalability and performance. The linear scaling performance is achieved on a single locality with varying data-structure sizes and on multiple localities. During the internship, she has utilized the HPX serialization library to bitwise serialize SHAD types. She also learned how to associate multiple tasks to the same handle, forming a task group, and run the callbacks on remote localities via customized actions.

Before that, she collaborated with PNNL for a scalable second-order optimization for deep learning applications. During the collaboration, she has implemented a PyTorch second-order optimizer and compared its performance with stochastic gradient descent (SGD), a first-order optimizer, on an image classification task, using a multi-layer perceptron network with one hidden layer. The scalable performance and improving throughput were achieved:  2.2x speedup was achieved over SGD in multi-thread scenario, and 5.8x speedup was achieved in multi-process scenario.

Previously, she implemented a scalable and distributed alternating least square (ALS) recommendation algorithm for large recommendation systems and a number of iterative solvers on the open source distributed machine learning framework, Phylanx. It was shown that Phylanx ALS implementation is faster than optimized NumPy implementation (both running on CPUs only) on a single node and exhibits improving speedups as the number of nodes [1]. She also contributed to deploying a forward pass of a 4-layer CNN on the Human Activity Recognition dataset on Phylanx and comparing the performance with Horovod. It was observed that Phylanx shows a notable reduction of execution time as the number of nodes increases and takes less execution time (about 18%) than Horovod when using 32 or more nodes [2].

Outside the lab, Nanmiao enjoys spending time in nature.  She likes hiking, camping,  snorkeling, and travelling. She also likes reading. Her favorite books of 2021 are Neapolitan Novels.

References:

[1] Steven R. Brandt, Bita Hasheminezhad, Nanmiao Wu, Sayef Azad Sakin, Alex R. Bigelow, Katherine E. Isaacs, Kevin Huck, Hartmut Kaiser, Distributed Asynchronous Array Computing with the JetLag Environment, The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2020.

[2] Hasheminezhad, Bita and Shirzad, Shahrzad and Wu, Nanmiao and Diehl, Patrick and Schulz, Hannes and Kaiser, Hartmut, Towards a Scalable and Distributed Infrastructure for Deep Learning Applications, 2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS), 2020.

Virtual Workshop Held by STE||AR Group

The SC16-001 Advanced Parallel Programming in C++ workshop was held on 7/25 as part of USNCCM16. Patrick Diehl, Hartmut Kaiser, and Steven R. Brandt of CCT/LSU were the instructors and organizers for this virtual event. Participation was very good, with 28 national and international attendees!

In the tutorial, participants learned how to use C++17 functions and objects to write straightforward yet fully parallized code without external tools, such as OpenMP. The team used Jupyter Notebooks with the Cling extension for C++ to walk attendees step-by-step through creating a fully parallized one-dimensional finite element code.

In the second half of the tutorial, the team demonstrated how users can employ nearly the same syntax to distribute these codes across a cluster. They used the HPX library to provide the support needed to manage a distributed application. 

The work-shop was very well received and will be offered again next year!

CPPCast Episode: HPX and DLA Future

CppCast, hosted by Jason Turner and JeanHeyd Meneide, is the first podcast for C++ developers by C++ developers. Since 2015 CppCast has been having conversations with C++ conference speakers, library authors, writers, ISO committee members and more.

Hartmut Kaiser and Mikael Simberg of the STE||AR Group joined Jason and JeanHeyd for a podcast episode recently. They discussed some blog posts on returning multiple values from a function and C++ Ranges. Then they talk about the latest version of HPX, how easy it is to gain performance improvements with HPX, and DLA Futures, the Distributed Linear Algebra library built using HPX.

To listen to the podcast episode, click here.

GSoC 21 – First Eval Status Update

In the first second 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:

Akhil Nair

My GSoC work mainly involves targeting the following three issues :-

I’ll be adapting the remaining algorithms in these issues so that they conform to the C++20 standard.

The project involves adding tag_dispatch and tag_fallback_dispatch CPO (Customization Point Object) overloads. Tag_dispatch overloads are used for the segmented overloads and tag_fallback_dispatch for the parallel overloads as this ensures that it tries to dispatch to a segmented overload even if it’s not an exact match before looking at the parallel overloads.

Range based overloads are also added as well as overloads taking in sentinel values. The base implementations are modified to support these sentinel values. Tests for these overloads as well as any missing tests for the parallel overloads have also been added. Doxygen documentation for each overload and the deprecation of the old hpx::parallel namespace overloads is also done.

The first phase of the GSoC period was great, the community was very supportive and nice. The weekly meetings are really helpful and something to look forward to every week and everyone is helpful and responsive on the irc channel. I am slowly getting to know my mentors as well as my fellow GSoC/GSoD students.

For the first phase of GSoC, I’ve adapted the following algorithms to C++ 20 :-

Merged:

Open:

Apart from this, I’ve also created a PR to add the ranges starts_with and ends_with algorithm.

Moving forward, I would be working on adapting the remaining algorithms (not a lot left now) and working on some other issues as well such as adding the shift_left and shift_right algorithms and looking into the performance issues of the scan partitioner.

Srinivas Yadav

Add vectorization to par_unseq implementations of Parallel Algorithms

An Overview of the Project

 Currently the HPX algorithms support data parallelism through explicit vectorization using Vc library and only for few algorithms like for_each, transform and count, but recently the support for Vc library has been deprecated and it is moved to std-experimental-simd. So the aim of the project is to adapt the data-parallel support for parallel algorithms using the new  std-experimental-simd.

Work Done So far

The existing support for data parallelism is tightly entangled with the implementation of the parallel base algorithms. So the first job was to separate the existing datapar algorithms by creating Customization Point Objects (CPOs) using tag_dispatch for datapar execution policies and fallback using tag_fallback_dispatch for other execution policies to the base implementation.

At first I really did not understand how the CPOs work and why they were used in HPX, but later soon I really got into understanding how they work with the help of my mentors and their references to some nice resources for C++ metaprogramming and CRTPs and turns out CPOs was the perfect solution for separating the datapar algorithms. The existing algorithms that support data parallelism rely on two main utility algorithms called util::loop and util::transform. So I had converted these two algorithms to CPOs which successfully separated the datapar algorithms. Here is the link to PR.

This first job really made me understand the existing implementation of the data parallel algorithms and helped me a lot in future in adapting other algorithms. Then I moved on to adapt the std-experimental-simd backend. This task mainly requires implementing four basic traits i.e vector_pack_type, load_store, size and count which can be used to adapt most parallel algorithms. It was fairly simple and I implemented them, but later the CI tests were failing for clang compilers after going through the errors we found out that std-experimental-simd was partially implemented for clang compilers and was only fully  implemented for GCC so then adding extra flags which enable datapar support only with GCC during cmake resolved this issue. Then I was told by one my mentors that there was proposal to C++23 for adding simd support to parallel algorithms which is the one of the main goals of this project, so in the proposal [P0350] the author has proposed the datapar execution policies with the name of simd execution policy, so after discussing with mentors we decided to rename the datapar and dataseq policies to simdpar and simd. Then after completing  this task the existing algorithms support with new simd and simdpar execution policies. Here is the link to PR.

I was playing around with the new std-experimental-simd and was testing out the performance for a few kernels then it turns out the performance when compared to the old Vc backend was really impressive and there were some nice speed ups. I created a new repo for the performance tests which were run on different machines with different architectures.

I was testing the performance only with one metric i.e speed up, but later I was told by my mentors that the roofline model is another way to measure the performance, which I was new to and I was lucky to get an explanation on it from one of my mentors and and understood that  which essentially shows whether the performance that we got out of the newly adapted simd algorithms is reaching CPU theoretical peak performance or not and shows the kernel used is compute bound or memory bound.

The following figure shows speed plot for one the algorithms (for_each using compute bound kernel)

X_axis : Number of elements in array (In powers of 2)

Y_axis : Speed up against sequential execution policy

What’s Next ?

The next step is to implement different compute bound and memory bound kernels and get the roofline plots for them to measure the performance and I still need to clean up the repo I created for the performance benchmarks, after that I will be working on adapting the remaining algorithms here to datapar.