High Performance Computing with Charm++
The 21st Annual Workshop on
Charm++ and Its Applications
April 25-26th, 2024
University of Illinois Urbana-Champaign
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).
ABOUT
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.IMPORTANT DATES
Category | Dates |
---|---|
Abstracts Due | March 29th, 2024 |
Author Notification | April 5th, 2024 (rolling basis for early submissions) |
Workshop | April 25-26th, 2024 |
KEYNOTE SPEAKER
David A. 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.
CALL FOR PRESENTATIONS
ORGANIZERS
Organized by: Parallel Programming Laboratory at the University of Illinois Urbana-Champaign