Recent & Upcoming Talks

Developments in Fundamentals of AI

Moderator: David Bader, Professor, New Jersey Institute of Technology
Craig Gotsman, Professor, College of Engineering, University of Miami
Mark Finlayson, Associate Professor, Computer Science, Florida International University
Yelena Yesha, Professor, Frost Institute for Data Science and Computing

The future of computing is being driven by the convergence of high-performance computing (HPC) and the growing potential of quantum …

Distinguished NJIT professor Dr. David Bader will will speak about the transformative potential of artificial intelligence (AI). Bader is known globally for his innovative study of both the history and the cutting edge of computing.

Join us on Wednesday, October 16 for an AI panel discussion lead by distinguished professor, Dr. David Bader. Following the panel, there will be a reception and networking opportunity for attendees.

Welcome: Senior Provost for Research Atam P. Dhawan

Host: Distinguished Professor Guiling “Grace” Wang, Associate Dean for Research

Learn insights and strategies from faculty and industry partners who have successfully secured funding from leading agencies.

About

The HPC Connection Workshop is an international High Performance Computing event organized by the Asia Supercomputer Community. This event takes place three times a year: during ASC in China, ISC in Germany, and SC in the USA. Top researchers and leading professionals from around the world gather at the workshops to discuss the latest developments and disruptive technologies in AI and supercomputing.

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for additional research on scalable algorithms and development of frameworks for solving these problems on high performance computers, and the need for improved models that also capture the noise and bias inherent in the torrential data streams. In this talk, Bader will discuss the opportunities and challenges in massive data-intensive computing for applications in computational science and engineering.

Moderator: David A. Bader

Panelists:

• Joe Corsi, Deputy CISO, Excellus BCBS
• Steve Hendrie, VP and CISO, The Hershey Company
• Richard Rushing, CISO, Motorola Mobilty
• Russ Wolfe, Business Information Security Officer, Navy Federal

Data analytics are an integral part of data science. The introduction of new scalable and high-performance data analytics frameworks, including the use of accelerators such as GPUs, has enabled data scientists to inspect their data and extract insights previously unattainable. In the last decade, the GPU has trailblazed this new exciting field of high-performance data analytics. This session will cover recent developments that have helped scale data analytics on the GPU to new heights. We will focus on analytics in social network analysis, bioinformatics, and cybersecurity, to name a few. We will also focus on other foundational components of scalable data analytics such as communication, data structures, algorithms, and ETL.

David A. Bader is a Distinguished Professor in the Department of Computer Science 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.

Supercomputers enable us to tackle some of the most challenging computational and data-intensive real-world problems facing our societies and the planet. In this talk, David A. Bader, the 2021 IEEE Computer Society Sidney Fernbach Award recipient, will share his passion for solving these global grand challenges. Bader revolutionized the computing industry by architecting the first Linux Supercomputer. Today, all the Top 500 supercomputers in the world are Linux HPC systems. His seminal contributions include the first general-purpose programming methodology for multicore clusters and accelerator technologies, such as his pioneering use of the Cell processor and GPUs. Bader is also recognized for his long-standing research efforts on novel parallel and streaming algorithms that have produced multiple “firsts” for graph traversal, search, centrality, and community detection, and many of the best-performing graph algorithms for GPUs. Bader is an innovator in academia as well. In 2005, Bader founded Computational Science and Engineering at Georgia Tech, created MS and PhD academic programs, and launched a successful new school. In 2019, Bader moved to New Jersey Institute of Technology as inaugural director of the Institute of Data Science, founded the Department of Data Science, and created one of the first B.S. degrees in Data Science.

This talk presents graph analytics designed and implemented for Arkouda, an open-source framework for providing NumPy-like Python functionality on large datasets.

Oliver Alvarado Rodriguez (presenter), Zhihui Du and David Bader; New Jersey Institute of Technology

Exploratory graph analytics is a much sought out approach to help extract useful information from graphs. One of its main challenges arises when the size of the graph expands outside of the memory capacity that a typical computer can handle. Solutions must then be developed to allow data scientists to efficiently handle and analyze large graphs in a short period of time, using machines that have the capacity to handle massive file sizes. Arkouda is a software package under early development created with the intent to bridge the gap between massive parallel computations and data scientists wishing to perform exploratory data analysis (EDA). The communication system between the Chapel back-end and the Python front-end helps to create an easy-to-use interface for data scientists that does not require knowledge of the underlying Chapel code and instead allows them to utilize the simple Python front-end to carry out all their large file and graph EDA needs. In this work, a graph data structure is designed and implemented into the Arkouda framework at both the Chapel back-end and the Python front-end. The main attraction of this data structure is its ability to occupy less memory space and perform efficient adjacency edge searching. A parallel breadth-first search (BFS) algorithm is also presented to help demonstrate how easily one can implement parallel algorithms in Arkouda to increase EDA productivity with graphs. Lastly, real-world graphs from different domains, such as biology and social networks, are utilized to evaluate the efficiency of the graph data structure and the BFS algorithm. The results obtained from this benchmarking help show that the Arkouda overhead is almost negligible, and data scientists can utilize Arkouda for large scale graph analytics. This work can help further bridge the gap between high-performance computing (HPC) software and data science to create a framework that is straightforward for all data scientists to use. All of the code in this project and in Arkouda is open source and can be found on GitHub. This is joint work with Mike Merrill and William Reus. We acknowledge the support of National Science Foundation grant award CCF- 2109988.

Panel Topic: Best practices for Data-Intensive applications

Panelists:

• Nirav Merchant, University of Arizona Data Science Institute
• David A. Bader, Institute for Data Science at the New Jersey Institute of Technology
• Sandra Gesing, US-RSE & Center for Research Computing of University of Notre Dame
• Daniel S. Katz, University of Illinois
• Gregory Dobler, University of Delaware Data Science Institute

Some interesting items to be discussed are:

David A. Bader
Distinguished Professor in the Department of Computer Science
Director of the Institute for Data Science at New Jersey Institute of Technology

Title: Solving Global Grand Challenges with High Performance Data Analytics

AGENDA Tuesday October 27

1:00 PM - 1:05 PM
Welcome and Overview (All Times are ET)

1:05 PM - 1:10 PM
NJIT Welcome from Dean
Craig Gotsman, Dean of the Ying Wu College of Computing

Government agencies across the globe are challenged with complex, ever-evolving problems. The answers exist somewhere in a vast amount of data, but they are only identifiable with the right technology to make sense of the interconnectedness within your data. That technology is a graph database.

This forward-looking panel will explore the future of CSE in a broad academic context. What new “grand challenges” may drive progress in CSE? How can CSE shape the future of new application fields such as computational medicine and biology, computational geoscience, and materials science? Are there opportunities to extend CSE to new areas such as social network analysis, cybersecurity and the social sciences, with mathematics-based large-scale computing rapidly becoming of crucial importance in almost all areas of society? Is the CSE paradigm and focus sufficiently unique and significant to warrant separate programs, graduate degrees, academic departments, and funding streams?

Supercomputing and Big Data: From Collision to Convergence

As data intensive science emerges, the need for high performance computing (HPC) to converge capacity and capabilities with Big Data becomes more apparent and urgent. Capacity requirements have stemmed from science data processing and the creation of large scale data products (e.g., earth observations, Large Hadron Collider, square-kilometer array antenna) and simulation model output (e.g., flight mission plans, weather and climate models). Capacity growth is further amplified by the need for more rapidly ingesting, analyzing, and visualizing voluminous data to improve understanding of known physical processes, discover new phenomena, and compare results.

DESCRIPTION

David Bader, School of Computational Science and Engineering Georgia Institute of Technology

David A. Bader, Aydin Buluc, John Gilbert, Joseph Gonzalez, Jeremy Kepner and Tim Mattson

Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in computational science and engineering, solving these problems at scale often raises new challenges because of sparsity and the lack of locality in the data, the need for additional research on scalable algorithms and development of frameworks for solving these problems on high performance computers, and the need for improved models that also capture the noise and bias inherent in the torrential data streams. In this talk, the speaker will discuss the opportunities and challenges in massive data-intensive computing for applications in computational biology, genomics, and security. The explosion of real-world graph data poses a substantial challenge: How can we analyze constantly changing graphs with billions of vertices? Our approach leverages fine-grained parallelism, lightweight synchronization, and shared memory, to scale to massive graphs.

Brief Biography

David A. Bader is a Full Professor in the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology, and Executive Director for High Performance Computing. Dr. Bader is a lead scientist in the DARPA Ubiquitous High Performance Computing (UHPC) program. He received his Ph.D. in 1996 from The University of Maryland, and his research is supported through highlycompetitive research awards, primarily from NSF, NIH, DARPA, and DOE. Dr. Bader serves on the Research Advisory Council for Internet2, the Steering Committees of the IPDPS and HiPC conferences, the General Chair of IPDPS 2010 and Chair of SIAM PP12. He is an associate editor for several high impact publications including the Journal of Parallel and Distributed Computing (JPDC), ACM Journal of Experimental Algorithmics (JEA), IEEE DSOnline, Parallel Computing, and Journal of Computational Science, and has been an associate editor for the IEEE Transactions on Parallel and Distributed Systems (TPDS). He was elected as chair of the IEEE Computer Society Technical Committee on Parallel Processing (TCPP) and as chair of the SIAM Activity Group in Supercomputing (SIAG / SC). Dr. Bader’s interests are at the intersection of high-performance computing and real-world applications, including computational biology and genomics and massive-scale data analytics. He has co-chaired a series of meetings, the IEEE International Workshop on High-Performance Computational Biology (HiCOMB), coorganized the NSF Workshop on Petascale Computing in the Biological Sciences, written several book chapters, and co-edited special issues of the Journal of Parallel and Distributed Computing (JPDC) and IEEE TPDS on high-performance computational biology. He is also a leading expert on multicore, manycore, and multithreaded computing for data-intensive applications such as those in massive-scale graph analytics. He has co-authored over 100 articles in peer-reviewed journals and conferences, and his main areas of research are in parallel algorithms, combinatorial optimization, massive-scale social networks, and computational biology and genomics. Prof. Bader is a Fellow of the IEEE and AAAS, a National Science Foundation CAREER Award recipient, and has received numerous industrial awards from IBM, NVIDIA, Intel, Cray, Oracle/Sun Microsystems, and Microsoft Research. He served as a member of the IBM PERCS team for the DARPA High Productivity Computing Systems program, was a distinguished speaker in the IEEE Computer Society Distinguished Visitors Program, and has also served as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor. Bader is recognized as a “RockStar” of High Performance Computing by InsideHPC and as HPCwire’s People to Watch in 2012.

Tarek El-Ghazawi and leading lights in the software field ask (and maybe answer) the question, “Can Developing Applications for Massively Parallel Systems with Heterogeneous Processors Be Made Easy(er)?”

Whether petascale computing can be sustained into the future; or whether power, space, and cost will restrict the number of sites around the world is the central question for SOS11. The SOS series of workshops aims to assemble interdisciplinary teams and collaboratories to develop the required, state-of-the-art mathematical algorithms and software, supported by appropriate hardware and middleware infrastructure to use petascale (and beyond) computing resources effectively to advance fundamental research in compelling scientific missions. The overarching goal of the first SOS meeting was to familiarize all participants with the overall integrated SOS program and to initiate team building both across DOE laboratories and with academia to support that integration across projects.

Joint work with Kamesh Madduri.

Workshop on Building Scalable Simulations of Complex Socio-Technical Systems

Complex socio-technical systems consist of a large number of interacting physical, technological, and human/societal components. Examples of such systems are urban regional transportation systems, national electrical powermarkets and grids, the Internet, ad-hoc communication and computing systems, public health, etc. Realistic social and infrastructure networks spanning urban regions are extremely large: consisting of millions of nodes and edges. As a result, the detailed simulations capable of representing such systems consist of millions of interacting agents. The challenges pertaining to design and implementations of such simulations on high performance computing platforms are unique, e.g., computation of extremely large dynamic unstructured composed networks. The workshop aims to bring together some of the leading researchers with the goal of identifying fundamental issues in designing, implementing and using such simulations on current and next generation high performance computing architectures. Examples of topics that will be covered include scalable HPC oriented design of such simulations; distributed algorithms and their implementations; formal specifications and simulation specific HPC system software.

Moderator: Craig Stewart, Indiana University
Panelists: Chris Johnson (Utah); John Reynders (Celera); David Bader (New Mexico); Debra Goldfarb (IDC); Rick Stevens (Argonne National Laboratories and the University of Chicago)

The biological/biomedical/bioinformatic/genomic/proteomic sciences now require the use of massive high performance computing (HPC) resources. In fact, biological sciences may more than double the size of the HPC market according to some estimates. This panel will be drawn primarily from the user community - people whose background is in the biological or chemical sciences - who have been successful in implementing HPC solutions to current biological and biomedical problems.

IEEE Outstanding Young Engineering Award Talk, Cosponsored by the Electrical & Computer Engineering Department