High Performance Computing with Charm++

The 21st Annual Workshop on
Charm++ and Its Applications

April 25-26th, 2024
University of Illinois Urbana-Champaign

Siebel Center for Computer Science, Room 2405

Keynote Speaker: David A. Bader
See the full workshop program here.
Join us remotely for Day 1, Day 2 and the Charm++ tutorial (no registration required).


The Charm++ Workshop

The workshop is broadly focused on adaptivity in highly scalable parallel computing. It also takes stock of recent results in adaptive runtime techniques in Charm++ and the collaborative interdisciplinary research projects developed using it.

The Charm++ Ecosystem

Charm++ is a C++ based parallel programming system based on an introspective adaptive runtime system, with many features suitable for addressing upcoming extreme scale as well as mid-scale challenges, and with multiple highly scalable parallel applications such as NAMD, ChaNGa, and OpenAtom.

Our group's goal is to develop technology that improves performance of parallel applications while also improving programmer productivity. We aim to reach a point where, with our freely distributed software base, complex irregular and dynamic applications can (a) be developed quickly and (b) perform scalably on machines with thousands of processors.


Category Dates
Abstracts Due March 29th, 2024
Author Notification April 5th, 2024 (rolling basis for early submissions)
Workshop April 25-26th, 2024


David A. Bader

David Bader

David A. Bader is a Distinguished Professor and founder of the Department of Data Science and inaugural Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. Dr. Bader is a Fellow of the IEEE, ACM, AAAS, and SIAM; a recipient of the IEEE Sidney Fernbach Award; and the 2022 Innovation Hall of Fame inductee of the University of Maryland’s A. James Clark School of Engineering. He advises the White House, most recently on the National Strategic Computing Initiative (NSCI) and Future Advanced Computing Ecosystem (FACE). Bader is a leading expert in solving global grand challenges in science, engineering, computing, and data science. His interests are at the intersection of high-performance computing and real-world applications...

David Bader

Arachne: An Open-Source Framework for Interactive Massive-Scale Graph Analytics

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of Arkouda, an open-source NumPy-like replacement for interactive data science on tens of terabytes of data. Massive-scale analytics is an emerging field that integrates the power of high-performance computing and mathematical modeling to extract key insights and information from large-scale data sets. Productivity in massive-scale analytics entails quick interpretation of results through easy-to-use frameworks, while also adhering to design principles that combine high-performance computing and user-friendly simplicity. However, data scientists often encounter challenges, especially with graph analytics, which require the analysis of complex data from various domains, such as the cybersecurity, natural and social sciences. To address this issue, we introduce Arachne, an open-source framework that enhances accessibility and usability in massive-scale graph analytics. Arachne offers novel algorithms and implementations of graph kernels for efficient data analysis, such as connected components, breadth-first search, triangle counting, k-truss, among others. The high-performance algorithms are integrated into a back-end server written in HPE/Cray’s Chapel language and can be accessed through a Python application programming interface (API). Arachne’s back-end server is compatible with Linux supercomputers, is easy to set up, and can be utilized through either Python scripts or Jupyter notebooks, which makes it a desirable tool for data scientists who have access to high performance computers. In this talk, Bader presents an overview of the algorithms his research group has implemented into Arachne and, if applicable, the algorithmic innovations of each. Further, Bader will discuss improvements to our graph data structure to store extra information such as node labels, edge relationships, and node and edge properties. Arachne is built as an extension to the open-source Arkouda framework and allows for graphs to be generated from Arkouda dataframes. The open-source code for Arachne can be found at https://github.com/Bears-R-Us/arkouda-njit. This is joint work with Oliver Alvarado Rodriguez, Zhihui Du, Joseph Patchett, Naren Khatwani, Fuhuan Li, Bader is supported in part by the National Science Foundation award CCF-2109988.


Authors are invited to submit abstracts describing research and development in the broad themes of parallel processing emphasized by Charm++, AMPI, and Charm4Py including runtime adaptivity, message-driven execution, task-based scheduling, automated resource management, and applications using these themes.

Some specific topics of interest include:
  • Parallel algorithms and applications, especially those using Charm++, AMPI, and Charm4Py
  • Parallel libraries and frameworks, especially those supporting runtime adaptivity
  • Novel parallel programming models and abstractions
  • Adaptive runtime management, including communication, power, energy, and heat
  • Dynamic load balancing
  • Within-node parallelism, tasks, and accelerators
  • Fault tolerance and resilience
  • Tools and techniques for performance analysis, tuning and debugging
  • Extensions and new features in Charm++
  • Massively parallel processing on future machines

  • Submissions should be in the form of extended abstracts ranging 1-3 pages. The presentations will be posted at the workshop website, but there will be no published proceedings. Authors are encouraged to publish them in other conferences or journals.

    Abstracts should be submitted through EasyChair by the deadline, March 29th, 2024. Early submissions are welcome and will expedite the reviewing process. Authors of early submissions will be notified of their acceptance within a week.

    Please contact Zane Fink and Matthias Diener if you have any questions.


    Organized by: Parallel Programming Laboratory at the University of Illinois Urbana-Champaign
    Program chair: Aditya Bhosale
    Local arrangements: Ritvik Rao and Zane Fink
    Audio and visual arrangements: Maya Taylor