Advanced Computational Technology Research

 Advanced Computational Technology image

In advanced computational technology research our program explores High-Performance Computing (HPC) and Quantum Computing. In HPC, it optimizes algorithms and architectures to enable complex simulations and scientific breakthroughs. Simultaneously, in quantum computing, it investigates quantum algorithms, programming languages, and system performance to push the boundaries of computation for cryptography, optimization, and the sciences. This dual-focused program aims to harness both classical HPC and emerging quantum capabilities, accelerating scientific progress and solving computationally intensive problems at unprecedented speeds.

Academics and Coursework

In addition to broader requirements for a CS undergraduate or graduate degree, students may specialize their education around Advanced Computational Technology research topics via these undergraduate and graduate level courses:

Undergraduate Courses

CSC 630 Computer Graphics Systems Design
CSC 641 Computer Performance Evaluation
CSC 647 Introduction to Quantum Computing and Quantum Information Science
CSC 656 Computer Organization

Graduate Courses

CSC 746 High Performance Computing
CSC 747 Introduction to Quantum Computing and Quantum Information Science
CSC 841 Computer Performance Evaluation
CSC 847 Cloud and Distributed Computing: Concepts and Applications
CSC 878 Big Data Platforms and Systems
CSC 890 Graduate Seminar – Quantum Computing
CSC 895 Applied Research Project
CSC 897 Research
CSC 898 Master's Thesis

Faculty and Focus Areas

Wes Bethel
High performance computing, quantum computing, scientific computing, computer graphics, scientific visualization, artificial intelligence/machine learning, computer vision/image analysis, computer organization and architecture
Daniel Huang
Programming languages, quantum computing, artificial intelligence/machine learning, computational chemistry

Publications and Reports

Balewski, J., Amankwah, MG, Beeumen, RV, Bethel, EW, Perciano, T., and Camps, D. (2024). Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs. Nature Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-53720-x

Burlen Loring, E. Wes Bethel, and Gunther Weber. Extensions to the SENSEI In situ Framework for Heterogeneous Architectures. In ISAV 2023: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, Denver, CO, USA, November 2023.

Daniel Huang, E. Wes Bethel, Talita Perciano, Roel van Beeumen, and Daan Camps. Towards High- Level Quantum Programming Languages. In ASCR Basic Research Needs in Quantum Computing and Networking, 2023.

Talita Perciano, Jan Balewski, Roel van Beeumen, E. Wes Bethel, Daan Camps, and Daniel Huang. A Quantum Approach for Efficient Biomedical Data Analysis. In ASCR Basic Research Needs in Quantum Computing and Networking, 2023.

Mercy Amankwah, Daan Camps, E. Wes Bethel, Roel Van Beeumen, and Talita Perciano. Quantum pixel representations and compression for N -dimensional images. Nature Scientific Reports, May 2022.

E. Wes Bethel, Burlen Loring, Utkarsh Ayatchit, David Camp, Earl P. N. Duque, Nicola Ferrier, Joseph Insley, Junmin Gu, James Kress, Patrick O’Leary, David Pugmire, Silvio Rizzi, David Thompson, Gunther H. Weber, Brad Whitlock, Matthew Wolf, and Kesheng Wu. The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale. In Hank Childs, Janine Bennett, and Christoph Garth, editors, In Situ Visualization for Computational Science. Springer, 2022.

E. Wes Bethel, Burlen Loring, Utkarsh Ayatchit, David Camp, Earl P. N. Duque, Nicola Ferrier, Joseph Insley, Junmin Gu, James Kress, Patrick O’Leary, David Pugmire, Silvio Rizzi, David Thompson, Gunther Weber, Brad Whitlock, Matthew Wolf, and Kesheng Wu. Proximity Portability and In Transit, M-to-N Data Partitioning and Movement in SENSEI. In Hank Childs, Janine Bennett, and Christoph Garth, editors, In Situ Visualization for Computational Science. Springer, 2022.

Peer-Temo Bremer, Georgia Tourassi, E. Wes Bethel, Kelly Gaither, Valerio Pascucci, and Wei Xu.Visualization for Scientific Discovery, Decision-Making, and Communication. In Summary Report from the ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, and Communication, United States, January 2022.

 B. Loring, E.W. Bethel, and K.J. Wu. Interfaces Supporting Data Management in Complex In transit Processing Workflows on Heterogeneous Systems with Deep Memory Hierarchies. In ASCR Workshop on the Management and Storage of Scientific Data, January 2022.

E. W. Bethel, B. Loring, O. Rübel, G. Weber, N. Ferrier, J. Insley, V. Mateevitsi, S. Rizzi, P. O’Leary, U. Ayachit, C. Wetterer-Nelson, E. Duque, and B. Whitlock. A Well-Designed Interface is a Trojan Horse for New Capabilities in Data Management and Data-intensive Processing. In ASCR Workshop on the Management and Storage of Scientific Data, January 2022.

G. H. Weber, E. W. Bethel, A. Butko, and John Shalf. Data Analysis on Heterogeneous Architectures at the “Edge” and Beyond. In ASCR Workshop on Visualization for Science, January 2022.

E. Wes Bethel, Colleen Heinemann, and Talita Perciano. Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel. In Matthew Larsen and Filip Sadlo, editors, Eurographics Symposium on Parallel Graphics and Visualization, Zürich, Switzerland, June 2021. 

E. W. Bethel, B. Loring, O. Rübel, G. Weber, N. Ferrier, J. Insley, V. Mateevitsi, S. Rizzi, P. O’Leary, U. Ayachit, C. Wetterer-Nelson, E. Duque, and B. Whitlock. Fostering Interoperability and Increasing Scientific Productivity in Environments of Heterogeneous Software and Computational Platforms. In ASCR Workshop on the Science of Scientific-Software Development and Use, October 2021.

H. Childs, S. Ahern, J. Ahrens, A. C. Bauer, J. Bennett, E. W. Bethel, et al.. A Terminology for In Situ Visualization and Analysis Systems. International Journal of High Performance Computing Applications,  August 2020.

T. Perciano, C. Heinemann, D. Camp, B. Lessley, and E. W. Bethel. Shared-memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives. In Proceedings of ISC High Performance 2020, Frankfurt, Germany, June 2020.

Burlen Loring, Mathew Wolf, James Kress, Sergei Shudler, Junmin Gu, Silvio Rizzi, Jeremy Logan, Nicola Ferrier, and E. Wes Bethel. Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization. In Steffen Frey, Jian Huang, and Filip Sadlo, editors, Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Norrköping, Sweden, May 2020.

Grants and Awards

US Department of Energy, Office of Advanced Scientific Computing Research. AQuA-DATA: Advanced Quantum Algorithms for Diverse Applications and Theoretical Advancements in Science. Oct 2024 -- Sep 2029. (SFSU PI: Bethel, SFSU Co-PI: Huang)

U. S. Department of Energy, Office of Advanced Scientific Computing Research. Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery (SENSEI). Subaward from Lawrence Berkeley National Laboratory. Oct. 2022 – Sep. 2024. (PI: Bethel)