NERSC staff at SC26
41Events
6Days
Nov 15, 2026 – Nov 20, 2026Program window
Sunday, November 15
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Tutorial
Deep Learning at Scale: Engineering High-Resolution Scientific AI Models
Full description
Deep learning is increasingly used in scientific applications involving high-dimensional, high-resolution data. As models, datasets, and data resolutions scale, so does the need for efficient, distributed training on high performance computing (HPC) systems. This tutorial addresses practical challenges in training scientific AI models on high-resolution data, including data throughput, memory pressure, and communication overhead in distributed HPC workflows. Participants will learn to profile and build efficient data pipelines, optimize single-GPU performance, and scale training across GPUs and nodes using data, tensor, and spatial (domain) parallelism. Through hands-on examples drawn from scientific use cases, we will show how different model families, such as transformers, convolutional neural networks, and neural operators, benefit from these parallelization strategies. We will emphasize the performance trade-offs among computation, memory, and communication that determine scalability for high-resolution scientific workloads. Attendees will gain experience running optimized workloads on GPU-based supercomputers and learn strategies they can apply to train scientific AI models that exceed single-device memory limits while scaling efficiently.- Presenters
- Shashank SubramanianLawrence Berkeley National Laboratory (LBNL)
- Steven FarrellLawrence Berkeley National Laboratory (LBNL)
- Corneel CasertLawrence Berkeley National Laboratory (LBNL)
- Adam RupeLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TUT — Tutorial Reg Pass
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Tutorial
Using Containers To Accelerate HPC
Full description
The use of containers has revolutionized the way in which industries and enterprises have developed and deployed computational software and distributed systems. This containerization model has gained traction within the HPC community as well with the promise of improved reliability, reproducibility, portability, and levels of customization that were not previously possible on supercomputers. This adoption has been enabled by a number of HPC container runtimes that have emerged including Apptainer, Shifter, Sarus, Podman, and others. This hands-on tutorial looks to train users on the use of containers for HPC use cases. We will provide a detailed background on Linux containers, along with an introductory hands-on experience building a container image, sharing the container and running it on an HPC cluster. Furthermore, the tutorial will provide more advanced information on how to run MPI-based and GPU-enabled HPC applications, how to optimize I/O intensive workflows, how to set up GUI-enabled interactive sessions, and how to use containers for agentic AI and MCP servers. Users will leave the tutorial with a solid foundational understanding of how to utilize containers on HPC resources using Podman, Docker, and Apptainer, and in-depth knowledge to deploy custom containers for HPC and AI on their own resources.- Presenters
- Adam LavelyLawrence Berkeley National Laboratory (LBNL); National Energy Research Scientific Computing Center (NERSC)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TUT — Tutorial Reg Pass
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Workshop
The 2nd International Workshop for Software Frameworks and Workload Management on Quantum-HPC Ecosystems
Full description
The integration of quantum computing (QC) with high performance computing (HPC) is emerging as a transformative paradigm for addressing complex scientific and engineering challenges. Given current quantum hardware limitations, such as noise, limited qubit counts, and constrained circuit depth, hybrid quantum–HPC (Q-HPC) ecosystems provide a practical path forward by combining complementary strengths. This workshop focuses on software frameworks, AI-enabled orchestration, and workload management strategies essential for scalable Q-HPC integration. Topics include architecture co-design, heterogeneous resource management, networking, fault tolerance, error mitigation, programming models, middleware, benchmarking, and large-scale distributed Q-HPC systems. Emphasis will be placed on open, interoperable ecosystems such as openQSE and on ML/AI techniques for adaptive workflow orchestration, topology-aware scheduling, performance prediction, and autonomous runtime control. Through invited talks, technical presentations, panels, and special sessions, the workshop will foster collaboration across academia, industry, national laboratories, and funding agencies to define research priorities and accelerate AI-driven Q-HPC ecosystems.- Session Chairs
- Katie KlymkoNational Energy Research Scientific Computing Center (NERSC); ESnet; Lawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
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Workshop
EESP 2026: 3rd International Workshop on Energy Efficiency with Sustainable Performance: Techniques, Tools, and Best Practices
Full description
The Energy Efficiency and Sustainable Performance (EESP) workshop fosters the exchange of innovative strategies, tools, and best practices to enhance energy efficiency in modern computing environments. As energy costs rise and CO₂ emissions increase, sustainable operation has become critical for HPC, AI, and data center infrastructures. EESP addresses the challenge of balancing performance, power, and sustainability, focusing on actionable, cost-effective solutions for Tier-0/1 and Tier-2 systems. The workshop promotes cross-layer innovation by integrating hardware, software, runtime systems, and data-driven analytics to achieve end-to-end visibility and adaptive energy management. It advances transparency in energy use and carbon emissions, supports hardware innovations (e.g., new architectures and liquid cooling), and enables dynamic adaptation, predictive analytics, and intelligent workload scheduling for real-time optimization. By fostering collaboration between HPC and AI communities, EESP advances reproducible practices and operational insights that empower computing centers, researchers, and industry to implement scalable solutions for long-term sustainable performance. EESP Workshop Link- Session Chairs
- Natalie BatesEnergy Efficient High Performance Computing Working Group (EE HPC WG); Lawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
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Workshop
High Performance Python for Science at Scale
Full description
This symposium-style workshop aims to connect researchers, developers, and Python practitioners to share their experiences scaling Python-based applications and workflows on supercomputers. The goal is to provide a platform for topical discussion of best practices, hands-on demonstrations, and community engagement via open-source contributions to new libraries, runtimes, and frameworks. Based on talks and demos that survey and summarize the best practices and recent success stories and developments, the workshop provides attendees a forum for expanding their knowledge of tools and techniques as well as opportunities to engage with representatives from the broader Python community.- Session Chairs
- Daniel MargalaLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
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Workshop
Enhancing Data Center Efficiency and Throughput by Co-Scheduling
Full description
Many HPC data centers have supercomputers with separate CPU-only and GPU compute nodes to support different workloads. One approach to increase system throughput is to run CPU-only jobs on unused CPU resources on the GPU nodes. Analysis of the workload running on NERSC's supercomputer showed that approximately 60% of Perlmutter's GPU node-hours employed fewer than 12 CPU cores and less than 128GB host memory. The remaining 52 CPU cores and 128GB host memory could be used by co-scheduled CPU-only jobs. However, it is critical that the co-scheduled CPU-only jobs and GPU jobs have controlled/bounded interference. This preliminary study assesses the feasibility of co-scheduling in production. Testing of the NERSC-10 benchmark applications on NVIDIA Grace-Hopper nodes using Memory System Resource Partitioning and Monitoring (MPAM) and network/InfiniBand quality of service for performance isolation measured a 70% throughput increase from co-scheduling compared with no co-scheduling, while maintaining an acceptable level of performance isolation.- Author/Presenters
- Jonathan SkoneLawrence Berkeley National Laboratory
- Dhruva KulkarniLawrence Berkeley National Laboratory
- Jack DeslippeLawrence Berkeley National Laboratory
- Brian AustinLawrence Berkeley National Laboratory
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Workshop
Custodian: Enabling Automatic Performance Engineering Agents on the NERSC HPC System
Full description
The advancement of Large Language Models and the rapid evolution of AI assistants and agents create an opportunity for the development of highly specialized HPC agents to enable automatic performance analysis. In this paper, we present Custodian, an HPC performance analysis agent that analyzes previously collected job telemetry and application-output data to characterize application behavior, identify potential performance bottlenecks, and transform performance metrics into natural language descriptions and figures that suggest optimizations or highlight computing inefficiencies. We validate Custodian’s telemetry-derived application states and roofline analyses against NVIDIA Nsight Systems and Nsight Compute across four representative applications executed on NERSC Perlmutter. Finally, we discuss the tradeoffs, cost, productivity and systems applicability of HPC agents on the NERSC HPC system.- Author/Presenters
- AbdulHannan MohammedLawrence Berkeley National Laboratory
- Dhruva KulkarniLawrence Berkeley National Laboratory
- Samuel WilliamsLawrence Berkeley National Laboratory
- Jack DeslippeLawrence Berkeley National Laboratory
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Workshop
Beyond FLOPS and Kernels: Benchmarking Agentic Science and the Future Role of Python
Full description
Python has become the dominant language for scientific computing, AI, and high-performance computing workflows. As supercomputing enters an era increasingly shaped by foundation models, autonomous agents, and AI-guided scientific discovery, the community faces a fundamental question: what should we benchmark next? This panel brings together leaders from HPC, AI, and scientific software to discuss how benchmarks can continue to influence system architecture in the age of agentic workflows. Should future benchmarks measure isolated kernels and application performance, or end-to-end scientific outcomes? How do we evaluate systems that combine simulation, machine learning, retrieval, reasoning, and autonomous decision-making? And what role should Python play as the orchestration layer for increasingly heterogeneous and distributed computing environments? Panelists will explore whether Python can serve as the foundation for next-generation benchmarks and how benchmark design can guide the architecture of future AI-enabled supercomputers.- Panelists
- Rollin ThomasLawrence Berkeley National Laboratory
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Workshop
Panel Discussion and Q&A: Challenges in AI-enabled Q-HPC Integration
Full description
SC26 has not supplied a description for this event.
- Session Chair
- Katherine KlymkoNational Energy Research Scientific Computing Center (NERSC)
- Moderator
- Katherine KlymkoNational Energy Research Scientific Computing Center (NERSC)
Monday, November 16
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Tutorial
Performance Tuning of HPC and ML/AI Applications with the Roofline Model on GPUs, APUs, and CPUs
Full description
The Roofline performance model offers an insightful and intuitive method for extracting the key execution characteristics of HPC and ML/AI applications and comparing them against the performance bounds of modern CPUs and GPUs. Its abilities to abstract the complexity of memory hierarchies and identify the most profitable optimization techniques have made Roofline-based analysis increasingly popular in the HPC and ML/AI communities. Although different flavors of the Roofline model have been developed, there remains a need for a systematic methodology when applying them to analyze applications running on multicore and accelerated systems. The tutorial aims to bridge this gap on both CPUs and GPUs by both exposing the fundamental aspects behind different Roofline modeling principles as well as providing several practical use case scenarios that highlight their efficacy for performance and energy optimization as well as understanding performance portability. This tutorial presents a unique combination of instruction in Roofline by its creator, hands-on instruction in using Roofline within NVIDIA’s, AMD’s, and Intel’s production performance tools, and discussions of real-world Roofline use cases at NERSC, ALCF, and OLCF. The tutorial presenters have a long history of collaborating on the Roofline model and have presented several Roofline-based tutorials.- Presenters
- Samuel WilliamsLawrence Berkeley National Laboratory (LBNL)
- Neil MehtaLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TUT — Tutorial Reg Pass
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Workshop
Enabling Performance Isolation for Co-scheduled Workflows
Full description
Heterogeneous systems such as NVIDIA GH200 and GB200 pair powerful GPUs with substantial CPU capacity, but GPU jobs targeting these systems frequently leave much of the CPU capacity idle, lowering CPU utilization and overall system throughput. Co-scheduling CPU workloads alongside GPU jobs improves resource utilization, but can introduce interference and undermine performance predictability. We present a proof of concept for co-scheduling CPU and GPU jobs on GH200 nodes with hardware-enforced resource isolation using Arm Memory System Resource Partitioning and Monitoring (MPAM) for CPU, L3-cache, and memory bandwidth and InfiniBand QoS for host network traffic. We augment existing resource isolation mechanisms in Slurm to allocate jobs to dedicated CPU and GPU "shards" with allocations for compute cores, L3-cache, memory bandwidth, and network bandwidth. This proof-of-concept implementation provides a practical framework for evaluating co-scheduled workloads with hardware-enforced resource isolation and establishes an operational model for multi-tenant co-scheduling on heterogeneous HPC/AI nodes.- Author/Presenters
- Daniel MargalaLBNL NERSC
- Jonathan SkoneLBNL NERSC
- Christopher SamuelLBNL NERSC
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Workshop
Stress-Testing Exascale Scientific Data Output - ADIOS2 Configuration Strategies for WarpX under Extreme Load Imbalance
Full description
Spatially imbalanced, temporally irregular simulation output challenges I/O configurations tuned for balanced workloads. This work evaluates ADIOS2 BP5 configurations through openPMD-api using WarpX on Frontier at ORNL and Perlmutter at NERSC, contrasting balanced 3D Cartesian output with boosted-frame back-transformed diagnostics (BTD). BTD combines rank-level output skew with snapshot contributions spanning multiple simulation steps. Balanced output reaches 3.64~TB/s without compression on 2,048 Frontier nodes and over 1~TB/s on 512 Perlmutter nodes; lossless Blosc2 compression achieves approximately 7~TB/s application-level effective throughput on Frontier. In the tested BTD configurations, SPAN combined with asynchronous writing provides the highest throughput on Frontier, FlattenSteps supports assembly of complete lab-frame snapshots for checkpointing without reader modification and with negligible write-time overhead and no penalty on read performance, and DataSizeBased aggregation improves throughput when subfile counts are constrained. These results identify complementary mechanisms for handling irregular output and provide configuration guidance for these workloads and systems.- Author/Presenters
- Axel HueblLawrence Berkeley National Laboratory
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Early Career
Mentorship in Practice: Building Relationships for Early-Career Growth
Full description
Early-career professionals are often told to “find a mentor,” with little guidance on where to start or how to navigate the relationship once they have one. This panel brings together professionals with experience as both mentors and mentees to discuss how these relationships begin, what makes them useful, what to talk about, and how they can evolve over time. Panelists will also discuss the distinction between mentors and sponsors and share their thoughts on building a broader network of people who can offer guidance, perspective, and advocacy at different career stages. The moderated discussion will be followed by an open Q&A, where attendees are invited to bring their own questions and experiences into the conversation.- Moderator
- Melissa RomanusLawrence Berkeley National Laboratory (LBNL)
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Workshop
Evaluation of LLVM Flang for Production HPC Applications and Modern Fortran Features
Full description
In 2025, LLVM released its first Flang Fortran compiler version considered ready for widespread evaluation. We know of no published assessment of Flang compiling a workload-derived portfolio of high-performance computing (HPC) applications. We address this gap using workload data from the National Energy Research Scientific Computing Center (NERSC), which supports more than 10,000 scientists on approximately 1,000 projects. We selected ten Fortran packages heavily used at NERSC with available source code and compiled them using recent versions of Flang on NERSC’s Perlmutter system. Seven compiled without code modifications, though some required build-system changes. Three compiled after minor source edits, mostly to address Fortran standard violations. We evaluated seven additional packages selected for their use or enablement of standard Fortran parallel features: multi-image execution and do concurrent. Six such codes compiled with most or all unit tests passing.- Author/Presenters
- Rahulkumar GayatriLawrence Berkeley National Laboratory
- Phillip ThomasLawrence Berkeley National Laboratory
- Brandon CookLawrence Berkeley National Laboratory
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Workshop
WHPC Career Pathways
Full description
Three women working in industry, national laboratories, and academia will share their career journeys and perspectives on navigating professional transitions, mentorship, challenges, and opportunities. This session offers practical insights for students and early-career professionals exploring diverse pathways in careers within HPC.- Presenters
- Rebecca Hartman-BakerNERSC
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Workshop
Evaluating OpenMP API Version 6.1 Target Kernel Language
Full description
The OpenMP API has steadily evolved into a comprehensive, highly portable alternative to vendor-specific APIs for GPU acceleration. The upcoming OpenMP 6.1 specification introduces two key features for code acceleration: access to dynamic shared memory and multidimensional, kernel-like programming. In this paper, we provide an overview of these newly standardized features for C++ and Fortran codebases. We implement a functional prototype based on the official LLVM infrastructure, extending both the Clang and Flang compilers to support these additions. We evaluate our implementation across several C++ and Fortran benchmarks, confirming that these new OpenMP capabilities effectively help close the performance and expressiveness gap with vendor-specific APIs. During the evaluation, we also observe a few remaining opportunities where OpenMP could become even more competitive for specific computational patterns.- Author/Presenters
- Rahulkumar GayatriLawrence Berkeley National Laboratory
Tuesday, November 17
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Demo
Agentic Scientific AI at Scale: From Data to Insight with ClearML
Full description
From a browser session, a scientist directs an AI agent through a scientific AI campaign on NERSC systems. Using ClearML as a unified platform for data management, HPC job submission, monitoring, experiment tracking, and visualization, the agent prepares training runs, orchestrates hyperparameter tuning, follows their progress, compares results, and explains the findings. The demo shows how ClearML’s MCP integration can make the full workflow accessible to AI agents while keeping the scientist in charge of the research decisions.- Speakers
- Steven FarrellLBNL
- Shashank SubramanianLBNL
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Posters
How the NERSC User Community Connects
Full description
High performance computing centers, such as NERSC, serve thousands of projects and users across many science domains and funding offices, yet staff have few systematic ways to see how these communities form, persist, and connect for planning and outreach. As an exploratory step toward surfacing actionable insights from allocation data, 2020–2026 Perlmutter allocation records are turned into yearly project and user networks, and community detection, weighted betweenness bridge detection, and liaison ranking. Null-model and matched-popularity tests confirm the structure is real and actionable: communities stay above the 95% null modularity band every year; about 10% of projects carry 66%–74% of cross-office connectivity; and the top 200 liaison candidates reach 69% of active users. To our knowledge, this is the first network analysis of an HPC facility’s community.- Authors
- Kevin GottLawrence Berkeley National Laboratory (LBNL)
- Rebecca Hartman-BakerLawrence Berkeley National Laboratory (LBNL)
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Posters
Performance Analysis of Phaseless Auxiliary Field Quantum Monte Carlo
Full description
Predicting molecular behavior requires knowing a molecule's ground-state energy, a calculation whose exact cost grows exponentially with system size. Phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) approximates this energy via guided random walks, where accuracy and cost depend heavily on the quality of the trial wavefunction used to steer the walk. We compare two quantum methods for preparing this trial wavefunction, the Variational Quantum Eigensolver (VQE) and the Generative Quantum Eigensolver (GQE), benchmarked on NERSC Perlmutter using linear hydrogen chains. At H8 (16 qubits), GQE reduces end-to-end pipeline wall-time by roughly 5x relative to ADAPT-UCCSD, while matching VQE-family accuracy near equilibrium bond length. These results motivate extending the study to H10 chains and multi-node scaling to determine whether GQE's cost advantage persists at larger, more strongly correlated systems.- Authors
- Neil MehtaLawrence Berkeley National Laboratory (LBNL)
- Katherine KlymkoLawrence Berkeley National Laboratory (LBNL)
- Ermal RrapajLawrence Berkeley National Laboratory (LBNL)
- Nickolas JoynerLawrence Berkeley National Laboratory (LBNL)
- Lisa ClausLawrence Berkeley National Laboratory (LBNL)
- Charles LivelyLawrence Berkeley National Laboratory (LBNL)
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Posters
Understanding the NERSC User Community Through Network Analysis
Full description
NERSC users collaborate, forming a network. Which users occupy the most structurally influential positions in the NERSC user community? Using 2020–2026 NERSC allocation records, a proxy collaboration graph was analyzed to reveal the user community’s dynamics. Two definitions of user connectivity were compared using available data on project allocation size: unweighted degree and repository-size-adjusted (RSA) strength. Degree distributions, program-office affiliation, and user roles were analyzed. Results show that RSA strength is a more accurate measurement of user connectivity than unweighted degree. The strongest connectors in the network are career scientists and users associated with high-energy physics. These findings suggest that in HPC network data, unweighted connectivity is not a reliable measurement of network influence or cross-disciplinary reach. Findings also suggest that allocation networks reflect certain field-specific collaboration patterns, implying that allocation networks may provide a useful proxy for co-authorship networks in the same field.- Authors
- Kevin GottLawrence Berkeley National Laboratory (LBNL)
- Rebecca Hartman-BakerLawrence Berkeley National Laboratory (LBNL)
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Demo
AI-assisted Operations of Data Acquisition Systems for Neutrino Experiments
Full description
This demonstration presents an AI Shifter for the NOvA Data Acquisition system deployed on NERSC resources. It combines real-time anomaly detection with an LLM-based solution engine grounded in experiment documentation and expert logbooks, helping operators diagnose problems and identify mitigation actions through natural-language interaction. LLM inference runs on Perlmutter compute nodes, while frontend and orchestration services run on Spin. The approach aims to improve data quality, reduce detector downtime, and provide a foundation for AI-assisted operations in DUNE and other high-energy physics experiments.- Speakers
- Pengfei DingLBNL
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Birds of a Feather
Confidential Supercomputing: Essential Evolution or Elegant Illusion?
Full description
Sensitive workloads across health sciences, financial modeling, and AI/ML on proprietary data increasingly demand confidentiality guarantees traditionally out of scope for supercomputing infrastructure. Confidential computing—hardware-enforced trusted execution environments established in cloud and edge contexts—is proposed as a path to securing computation at HPC scale. But is this achievable, or does it conflict with the principles that make supercomputers fast? This BoF gathers the HPC community to debate whether confidential supercomputing is a necessary, achievable evolution or an aspirational posture satisfying compliance optics without meaningful security. Join us to map where it genuinely delivers—and where gaps remain.- Additional Session Leaders
- Hai Ah NamLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
Wednesday, November 18
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Birds of a Feather
Navigating Complexity: Achieving Performance Portability in the Evolving Landscape of Accelerated HPC Systems
Full description
With the increasing demand for AI in HPC, there has been a rapid rise in accelerated architectures, portable programming models, and frameworks. The already-daunting task of programming for interconnected accelerated systems has become even more complex. This BoF, organized by IXPUG, will focus on portable programming across a wide range of heterogeneous architectures—including Intel, NVIDIA, AMD, and ARM supporting diverse simulation, data analytics, and AI workloads. The session will explore key challenges, state-of-the-art solutions, and emerging best practices for programming across these systems, identifying common principles and methodologies that support development and long-term maintenance across sites, architectures, and scientific applications.- Additional Session Leaders
- Neil MehtaLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
OpenQSE: Building an Open Quantum-HPC Software Ecosystem
Full description
Quantum computing resources are increasingly integrated into HPC and cloud environments, but software stacks remain isolated, vendor-specific, and often proprietary. OpenQSE is a community effort developing an open, modular ecosystem of vendor-neutral specifications to close the interoperability gaps that fragment hybrid quantum-classical workflows. This BoF will briefly share the initiative's roadmap and working-group progress, then open the floor for structured discussion with HPC facility staff, application teams, vendors, and researchers to identify priorities and shape the direction together. The intended outcome is a prioritized set of integration artifacts the community identifies as most urgent for near-term development.- Additional Session Leaders
- Ermal RrapajLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
Operational Data Analytics: Agentic AI for HPC Facilities
Full description
Operational data analytics (ODA) has become essential for understanding and optimizing HPC systems. However, converting the vast amounts of operational data they generate into actionable insights still requires significant manual effort and domain expertise. Agentic AI has the potential to extend traditional analytics by reading telemetry, correlating signals, diagnosing problems, and drafting fixes for an operator to approve. This BoF will explore the state of the art in agentic AI’s applicability to ODA, promising use cases, limitations, and governance challenges. Participants will share experiences and discuss how agentic AI can enhance future HPC operations and efficiency.- Additional Session Leaders
- Steven FarrellLawrence Berkeley National Laboratory (LBNL)
- Melissa RomanusLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Panel
The Future of the HPC Workforce: Skills, Scale, and Sustainability
Full description
High performance computing (HPC) is undergoing rapid transformation with the convergence of simulation, AI, data-intensive science, and emerging quantum computing. This evolution is reshaping the skills required to effectively use and advance these systems, creating urgent challenges in workforce development. This panel brings together perspectives from national laboratories, academia, and industry to discuss how to build a sustainable, inclusive, and future-ready HPC workforce. Key questions include: What will the HPC workforce need to look like in the next 5–10 years? Where are the most critical gaps in skills, training pathways, and access? What skills are currently missing or undervalued in existing curricula? Which collaboration models across institutions have proven effective? How can we broaden participation and lower barriers to entry? The session will include interactive audience engagement, with opportunities to submit and prioritize questions in real time, ensuring a dynamic and relevant discussion.- Panelists
- Rebecca Hartman-BakerNational Energy Research Scientific Computing Center (NERSC)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
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Birds of a Feather
Quantum Jumps: From NISQ to FTQC to Hybrid HPC
Full description
Quantum computing is experiencing accelerating transitions, from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computers, and from quantum computers being objects of research to their becoming scientific computation tools. Quantum programming habits and assumptions must be revisited, and for the HPC community, this is more than an incremental hardware upgrade; it affects entire stacks. This session will explore how compilation workflows, execution models, and algorithmic strategies are expected to change to incorporate error correction and integrate with production HPC workflows. Thought leaders from the quantum industry, HPC, and academic research will exchange perspectives and engage the audience in discussion.- Additional Session Leaders
- Katherine KlymkoLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
Thursday, November 19
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Panel
HPC–Quantum Integration: Software, Systems, and Hybrid Workflows
Full description
High performance computing (HPC) and quantum computing are coming together to enable hybrid workflows that combine classical and quantum resources within a single computing environment. As quantum technologies evolve at different paces, integrating them into HPC systems introduces challenges for software, hardware, and scientific applications. This panel brings together perspectives from HPC centers, quantum hardware development, and academic research to examine how HPC–quantum integration is approached in practice. Panelists will discuss software and systems layers enabling hybrid execution, including programming models, workflow orchestration, and middleware connecting HPC environments to quantum resources. Drawing on early integration efforts, the discussion will highlight how communities are engaging with hybrid workflows — from deploying and evaluating quantum resources within HPC facilities to developing hardware and exploring algorithms and use cases. Panelists will share insights into challenges such as latency, resource management, as well as differing expectations around performance, usability, and readiness across quantum paradigms.- Panelists
- Katherine KlymkoLawrence Berkeley National Laboratory
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
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Paper
GLOW: Accelerating Geometric Multigrid Layers on Cerebras Wafer-Scale Engine
Full description
This paper describes the mapping of geometric multigrid (GMG) onto the Cerebras Wafer Scale Engine (WSE-3). The WSE wafer integrates almost a million cores along with a low-latency 2D mesh network, providing unprecedented on-chip bandwidth and fine-grained communication. Although the architecture is predominantly used for deep learning, we explore the suitability of WSE for accelerating GMG. GMG solves sparse linear systems efficiently by recursively coarsening the grid, which leads to more rapid convergence with less computation. As compared to prior implementations of stencils on WSE, this demonstration highlights the challenges of mapping a layered algorithm – and the full set of operators – onto a dataflow architecture. We compare the performance of this implementation to that of the HPGMG benchmark on an NVIDIA GH200 GPU. Across different GMG configurations and problem sizes, we observe a speedup of up to 25× as compared to a single GH200 node.- Authors
- Samuel WilliamsLawrence Berkeley National Laboratory (LBNL)
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Birds of a Feather
Bringing Quantum Computing to the HPC Datacenter
Full description
As quantum computing matures, challenges with the adoption of this technology shift from theoretical feasibility to practical deployment within established supercomputing infrastructures. This BoF examines the holistic framework required to operationalize quantum systems, from the physical datacenter floor requirements to the high-level application layer. We dive into the complex interplay between infrastructure installation, platform requirements, security protocols, and the necessary evolution of the software stack to support quantum algorithms and hybrid QC-HPC workflows. As such, this BoF outlines a strategic blueprint for building resilient, high-performance quantum-classical hybrid computing environments suitable for next-generation scientific discovery.- Session Leader
- Ermal RrapajLawrence Berkeley National Laboratory (LBNL)
- Additional Session Leaders
- Katherine KlymkoLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
Designing Open, Heterogeneous System Monitoring Around OpenCHAMI
Full description
While modern management platforms like OpenCHAMI streamline cluster orchestration, they do not inherently provide the comprehensive monitoring and data collection required to observe every facet of a next-generation data center. Operating heterogeneous, large-scale systems requires a unified, open-source observability framework capable of aggregating distributed logs, metrics, and traces across thousands of bleeding-edge compute nodes. This BoF session brings together HPC systems engineers, data center architects, and site reliability engineers to discuss the architecture, integration challenges, and best practices for building robust telemetry pipelines around OpenCHAMI, ensuring operational visibility, rapid fault isolation, and open-source compliance in next-generation supercomputing environments.- Additional Session Leaders
- John StileLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
HPC User Support in the AI and Exascale Era
Full description
As HPC enters an era of extreme heterogeneity and AI integration, user support is a critical strategic enabler. However, rising system complexity and finite staffing strain traditional models. To thrive in the AI and exascale era, a collaborative community approach is essential. This BoF session invites practitioners, providers, and users to collaboratively reshape HPC support. Featuring insights from facility leaders on managing rapid AI adoption, an interactive live survey, and open group discussions, participants will share experiences regarding scaling, AI automation, and knowledge transfer. Outcomes will form a post-conference report dedicated to fostering a sustainable, community-driven support ecosystem.- Additional Session Leaders
- Rebecca Hartman-BakerLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
Real-Time Scientific Data Streaming to HPC Nodes: Challenges and Innovations VI
Full description
Emerging scientific needs are driving a new class of workflows that require near real-time processing, urgent computing, and time-sensitive decision-making. The traditional "compute-store-analyze" paradigm, which relies on dumping massive datasets to file systems for post-processing, has become an unsustainable bottleneck. It introduces unacceptable latency, and often renders high-frequency data analysis practically impossible. This BoF discusses this challenge by exploring the architectural shift from file-based workflows to high-performance streaming. In this new paradigm, storage is treated not as a buffer for post-processing, but as a sink for final scientific results, while analysis, visualization, and reduction occur in-transit or in-situ.- Session Leader
- Bjoern EndersLawrence Berkeley National Laboratory (LBNL); National Energy Research Scientific Computing Center (NERSC)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
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Birds of a Feather
Watts, FLOPs, and Impact: Rethinking Energy in the Exascale Era
Full description
Energy consumption has become a key constraint in high performance computing (HPC), shaping system design, procurement, software, and scientific workflows in the exascale era. As large-scale simulations and AI-driven workloads increase power demand, the community must reconsider how performance is measured, optimized, and reported beyond FLOPs alone. This BoF session will convene researchers, facility operators, vendors, and application scientists to discuss energy measurement, performance-per-watt metrics, efficient algorithms and runtimes, workload scheduling, and carbon-aware computing. The session will identify practical gaps, share lessons from production systems, and define community priorities for sustainable, energy-efficient HPC at scale.- Additional Session Leaders
- Natalie BatesLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
- TUT — Tutorial Reg Pass
- XO/EX — Exhibits Reg Pass
Friday, November 20
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Panel
Accelerated Quantum Supercomputing: AI, HPC, and the Quantum Applications Worth Betting On
Full description
Quantum processors are not useful in isolation. Today's small-scale QPUs require only modest classical hardware, but as systems scale toward utility and fault tolerance, dependencies on classical HPC and AI grow rapidly. These dependencies span the full stack: real-time decoders for fault-tolerant error correction, AI-assisted state preparation and circuit synthesis, hybrid quantum-classical algorithms, HPC-scale data processing, and verification. The serious questions for the SC community span all timelines: Which applications can quantum-HPC-AI integrated systems meaningfully accelerate now, which become reachable with fault tolerance, and which remain infeasible despite widespread touting? Integration approaches differ widely — RIKEN's Fugaku-IBM/Quantinuum hybrid platform, DOE lab user programs at NERSC and ORNL, NVIDIA's NVQLink GPU-QPU interconnect, and Google and AWS's standalone fault-tolerant roadmaps with classical co-processing serving error correction. These distinct framings reflect different visions for HPC-AI integration, and our community lacks consensus on which application and architecture pairings are optimal.- Panelists
- Katherine KlymkoLawrence Berkeley National Laboratory (LBNL); NERSC
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
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Workshop
Eighth Workshop on Interactive and Urgent High Performance Computing
Full description
Interactivity enables the exploitation of HPC in new and revolutionary ways, delivering many new and exciting opportunities for our community. Interactive HPC involves users being in the loop during job execution monitoring a job, steering the experiment, or visualizing results to make immediate decisions. Likewise, urgent computing combines interactive computational modeling with time-sensitive systems in the real world such as the near-real-time analysis of unfolding disasters to inform real-time decision making. Supporting interactive and urgent workloads on HPC requires expertise in a wide range of areas and the solving of numerous technical and organizational challenges. This workshop brings together stakeholders, researchers, and practitioners from across interactive and urgent computing within the wider HPC community. We will share success stories, case studies, and technologies to continue community building around leveraging urgent and interactive HPC as an important tool for scientific research, responding to disasters, and addressing societal issues.- Session Chairs
- Bill ArndtLawrence Berkeley National Laboratory (LBNL); National Energy Research Scientific Computing Center (NERSC)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
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Workshop
Eighth Workshop on Interactive and Urgent High Performance Computing (WIUHPC)
Full description
Interactivity enables the exploitation of HPC in new and revolutionary ways, delivering many new and exciting opportunities for our community. Interactive HPC involves users being in the loop during job execution monitoring a job, steering the experiment, or visualizing results to make immediate decisions. Likewise, urgent computing combines interactive computational modeling with time-sensitive systems in the real world such as the near-real-time analysis of unfolding disasters to inform real-time decision making. Supporting interactive and urgent workloads on HPC requires expertise in a wide range of areas and the solving of numerous technical and organizational challenges. This workshop brings together stakeholders, researchers, and practitioners from across interactive and urgent computing within the wider HPC community. We will share success stories, case studies, and technologies to continue community building around leveraging urgent and interactive HPC as an important tool for scientific research, responding to disasters, and addressing societal issues.- Organizers
- Will ArndtNERSC
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Workshop
Workshop on Operational Data Analytics in HPC (HPC-ODA)
Full description
Operational data analytics (ODA) provides unique opportunities to analyze, understand, and optimize HPC operations. Readily available open-source frameworks make the collection of telemetry from different operational domains of HPC increasingly easy. However, analyzing the data, extracting actionable knowledge from it, and leveraging it to optimize HPC operations is challenging. While most HPC sites have sophisticated monitoring capabilities in place, their analysis capabilities often do not go beyond visual inspection by humans via dashboards or static thresholds for alarms. Few early adopter sites have pioneered advanced usage of data, but its wider adoption will depend on success stories and the availability of analysis tools and models. This workshop will bring together HPC users, practitioners in HPC operations, monitoring experts, and data analytics researchers to share experiences, discuss state-of-the-art research, and foster collaboration. It will solicit papers with concrete, reproducible, and empirical data gathered through case studies, research for practice, and surveys.- Session Chairs
- Melissa RomanusLawrence Berkeley National Laboratory (LBNL)
- Recordings
- Livestreamed, Recorded
- Registration Categories
- TP — Tech Program Reg Pass
- W — Workshop Reg Pass
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Workshop
Measuring the Impact of the Better Scientific Software Fellowship Program
Full description
The Better Scientific Software Fellowship (BSSwF) Program was launched in 2018 in the U.S. to recognize, develop, and support leaders advancing practices, processes, and tools for better scientific software. Over eight years, the program has supported 43 Fellows and 43 Honorable Mentions across institutions and career stages to advance developer productivity and software sustainability and strengthen the scientific software community. A 2025 impact assessment examined program reach, contributions to developer productivity and software sustainability, and effects on careers, mentorship, and community engagement. Based on a landscape analysis, review of Fellow projects, and interviews with Fellows, findings indicate that the program expanded professional networks and visibility and created opportunities for collaboration, mentoring, and career advancement. Program elements include community-facing projects, professional engagement, mentoring, and dissemination of Fellows’ work. BSSwF provides a model and insights for training, professional engagement, networking, and workforce-development programs supporting individual careers and the broader scientific software ecosystem.- Author/Presenters
- Adam LavelyLawrence Berkeley National Laboratory
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Workshop
Perlmutter-Agent: Design and Early Deployment of a Local, Tool-Grounded LLM Assistant for HPC Incident Investigation
Full description
HPC incident investigation requires system administrators to correlate metrics, logs, and ticket history across heterogeneous data sources. We present Perlmutter-Agent, a locally deployed, tool-grounded large language model (LLM) assistant for incident investigation on NERSC’s Perlmutter supercomputer. An orchestrator converts Slack messages into structured requests and dispatches them to specialized metrics, logs, and ticket sub-agents. Guided by site-specific agent skills, the sub-agents query operational data through MCP servers and return evidence that the orchestrator summarizes into likely causes. All agents use an open-weight Qwen3.8-27B model served locally by vLLM, keeping operational data from external model providers. In a restricted beta, 339 of 380 user requests (89.2\%) completed successfully; diagnostic accuracy and operator usefulness remain to be evaluated.- Author/Presenter
- Siqi DengLawrence Berkeley National Laboratory (LBNL)
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Workshop
Towards MPI Communication Modeling From Passive Network Telemetry
Full description
The distribution of MPI message sizes issued by an application is a first-order determinant of its communication behavior, yet recovering this distribution typically requires application-level instrumentation and/or a priori knowledge of the communication pattern. We present an application-agnostic model of MPI communication that reconstructs the MPI message-size distribution from passive network telemetry: packet-level counters that modern NICs expose and that facility operators already collect continuously, system-wide, at negligible cost. The model requires no knowledge of the network topology, the communication peers, or the MPI operations in use, and can be applied retroactively to archived telemetry. Instantiated on the Cassini counters of NERSC's Perlmutter, the model estimates OSU Micro-Benchmark message counts within 0.5% and the dominant LAMMPS size classes within 10%, which is a first step toward instrumentation-free prediction of communication time.- Author/Presenters
- Rui LiuLawrence Berkeley National Laboratory
- Dhruva KulkarniLawrence Berkeley National Laboratory
- Zhengji ZhaoLawrence Berkeley National Laboratory
- Brian AustinLawrence Berkeley National Laboratory
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Source: SC26 Conference Program. Program details may change.
Last edited: November 9, 2026
