Workshops and tutorials day. Rooms and detailed times are coming soon unless noted below.
| Time | What |
|---|---|
| Morning |
Workshop: 1st Workshop on ML for Assisting Code Quality (MLAC). Chair: Jay Lofstead, Sandia National Laboratories. |
| Morning |
Tutorial: CEDR: A Holistic Software and Hardware Design Environment for Hardware Agnostic Application Development and Deployment on FPGA-Integrated Heterogeneous Systems. Presenters: Serhan Gener, Umut Suluhan, Ali Akoglu. |
| Morning |
Tutorial: SODA Synthesizer: Accelerating Artificial Intelligence Applications with an End-to-End Silicon Compiler. Presenters: Nicolas Bohm Agostini, Vito Giovanni Castellana, Fabrizio Ferrandi, Serena Curzel, Ankur Limaye, Antonino Tumeo. |
| All day |
Workshop: 1st National Science Data Fabric Summit. Organizers: Michela Taufer, University of Tennessee, Knoxville; Valerio Pascucci, University of Utah. |
| Afternoon |
Workshop: 1st LACS — Learning-Augmented Compilers & Systems. Organizers: Eun Jung (EJ) Park, Riyadh Baghdadi, Joseph Manzano, Keren Zhou. |
| Afternoon |
Tutorial: Chameleon: An Experimental Testbed for Parallel, Heterogeneous, and AI-Accelerated Computing. Presenters: Kate Keahey, Marc Richardson. |
| Afternoon |
Tutorial: Reproducible Benchmarking for High-Performance Computing Applications. Presenters: Olga Pearce, Gregory Becker, Doug Jacobsen, Stephanie Brink |
| 5:00pm-6:00pm | Conference reception. |
| Time | What |
|---|---|
| Morning | Opening, keynote, and technical sessions: Coming soon. |
| Midday | Lunch/break: Coming soon. |
| Afternoon | Technical sessions: Coming soon. |
| Evening |
Poster presentation and ACM Student Research Competition. Time and room: Coming soon. |
| Time | What |
|---|---|
| Morning | Keynote and technical sessions: Coming soon. |
| Midday | Lunch/break: Coming soon |
| Afternoon | Technical sessions: Coming soon. |
| Evening | Conference banquet. Location to be defined. |
| Time | What |
|---|---|
| Morning | Technical sessions and ACM SRC activities: Coming soon. |
| Midday | Closing session: Coming soon. |
Tuesday, October 20, 2026
Andrew A Chien
Univ of Chicago and Argonne National Lab
Chicago UpDown Computing, Inc.
Supercomputers and AI compute are ill-suited for sparse and data-intensive computations because they are optimized for maximum dense matrix "FLOPS". We have designed the UpDown System – optimized for graph computing, streaming data ingestion and transformation as well as high-level programming. The result outperforms conventional CPU/GPU-based systems by 10-100x on an ISO-power basis.
UpDown's radical micro-architecture unleashes fine-grained parallelism: 1-cycle thread creation and management, 1-cycle messages. This enables efficient computation on 10-instruction thread invocations. Further, software-controlled split-transaction DRAM access unlocks the power of HBM's massive memory bandwidth. For irregular applications, UpDown datapath efficiency is 10x greater. UpDown performance on skewed-graph computations exceeds multicore CPU's (>100x) and GPU's (20-60x) in single-node configuration. Updown performance scales to 1,000 and 10,000-fold speedup on BFS, Pagerank, Triangle Count, K-truss and more.
UpDown data ingestion exceeds 5 billion/records/s/node (1000x CPU-based databases), reaching 100 trillion records/s. It enables a new class of streaming analytics and complex workflows. UpDown enables high level programming with a global address space, and a flexible map-reduce framework (KVMSR) coupled with an event-driven language (UDWeave). This enables easy vertex, edge-centric programming, and fits well for other data-parallel models such as relational/graphDB, sparse matrixes, and more.
UpDown was created under funding from IARPA's AGILE program, and is being commercialized by Chicago UpDown Computing, Inc. (www.chupdown.com).
Andrew A Chien is the William Eckhardt Distinguished Service Professor of Computer Science at the University of Chicago and Senior Scientist at Argonne National Laboratories. Chien led the IARPA funded "UpDown System Project", designing breakthrough scalable graph analytics systems and is now Founder and President of Chicago UpDown Computing, Inc. (www.chupdown.com). He has led the Zero-carbon Cloud project since 2015, and is known for his research on datacenters, renewable energy and sustainability, cloud resource management and software, and large-scale system architecture. Chien has received numerous recognitions for research. Dr. Chien currently serves on the NSF CISE Advisory Committee and DARPA ISAT. He is a Fellow of the ACM, IEEE, and AAAS. He served as EiC of Communications of the ACM, 2017-2022, and Vice President of Research at Intel Corporation from 2005-2010. He served as SAIC Chair Professor of University of California, San Diego (1998-2005) and as faculty at the University of Illinois (1990-98). He received BS, MS, and PhD degrees from the Massachusetts Institute of Technology.
Wednesday, October 21, 2026
Josep Torrellas
Thomas M. Siebel Chair in Computer Science
Director, SRC JUMP 2.0 ACE Center for Evolvable Computing
University of Illinois, Urbana-Champaign
iacoma.cs.uiuc.edu/josep/torrellas.html
Given current energy-consumption trends, there is ample consensus that we will have to move much of the computation to hardware accelerators. This is because accelerators are the most energy-efficient platforms. However, from the evidence of past efforts in this direction, architecting an accelerator-centric computing environment looks very challenging. It is unclear what architectural designs and software advances will really enable this new paradigm. In this talk, I will outline our vision of the hardware and software needed for a successful accelerator-centric computing environment, and some of the efforts that we are doing in this direction.
Josep Torrellas is the Thomas M. Siebel Chair in Computer Science at the University of Illinois, Urbana-Champaign (UIUC). He is the Director of the ACE Center for Evolvable Computing (an SRC/DARPA JUMP 2.0 Center), past Co-Leader of an Intel Strategic Research Alliance (ISRA) on Computer Security, and past Director of the Illinois-Intel Parallelism Center (I2PC). His research interests are multiprocessor computer architectures and parallel computing. Some of his contributions include thread-level speculation (TLS) architectures, the Bulk Multiprocessor concept, deterministic record and replay mechanisms, process variation mitigation techniques, and hardware defenses against speculative execution attacks. In addition, he has contributed to several experimental multiprocessor designs such as IBM's PERCS Multiprocessor, Intel's Runnemede Extreme-Scale Multiprocessor, Illinois Cedar, and Stanford DASH.
Torrellas has received the IEEE Computer Society (CS) Harry H. Goode Memorial Award, the UIUC Daniel C. Drucker Eminent Faculty Award, the UIUC Campus Award for Excellence in Graduate Student Mentoring, the IEEE CS Edward J. McCluskey Technical Achievement Award, and was a Willett Faculty Scholar at UIUC. He is an IEEE CS Golden Core Member, and a Fellow of IEEE, ACM, and AAAS. He was the Chair of the IEEE Technical Committee on Computer Architecture (TCCA). He has served in the Board of Directors of the Computing Research Association (CRA) and has been a Council Member of CRA's Computing Community Consortium (CCC). He was a member of the U.S. National Academies Board on Army Research and Development. He serves in the International Roadmap for Devices and Systems (IRDS). Torrellas has graduated 53 PhDs. He received a PhD from Stanford University.
Thursday, October 22, 2026
Mary Hall
Professor, Kahlert School of Computing, University of Utah
Data movement is the dominant execution and energy cost across the application workloads in data centers and supercomputers. Programming at the tile level has become a popular strategy for optimizing data movement for both deep learning and general structured grids, using Triton, cuTile, bricks, and fine-grained data blocks. Expressing hierarchical data and thread layouts, mostly designed with matrix processors in mind, facilitates automatic code generation that further raises the level of abstraction in such code. In this talk, we will describe prior work on BrickLib supporting fine-grained data blocks and active research on LEGO for hierarchical data and thread layout. We will connect these concepts with emerging hardware features and future demands on programming systems to reduce data movement.
Mary Hall is a Professor and former Director of the Kahlert School of Computing at University of Utah. Her research focuses on high-performance computing, compiler optimizations and code generation for novel and emerging hardware, and performance tuning. She has served on the Board of Directors of the Computing Research Association since 2015, and she is currently its Vice Chair. She is an ACM Distinguished Scientist and an IEEE Fellow.
Conference Papers:
ACM SRC:
Conference: October 19–22, 2026