The HPC and Data Science Summer Institute is a comprehensive week-long workshop that covers introductory-to-intermediate topics in HPC, data science, and artificial intelligence (AI). It aims to give attendees a thorough overview of these topics to accelerate their learning process through highly interactive classes and hands-on tutorials on the Expanse supercomputer. A limited amount of travel support is available for this program.

The purpose of the Summer Institute is to give the attendees an overview of topics in High Performance Computing and Data Science and accelerate their learning process through highly interactive classes with hands-on tutorials on the Expanse Supercomputer.  Moreover, the attendees will have many opportunities to meet one-on-one with SDSC’s experts to discuss in detail the best techniques to solve their specific scientific problems. In order to benefit from the classes, the attendees are required to have familiarity with the UNIX/Linux shell. Basic programming skills (in any programming language) are strongly recommended.

Participation in the SDSC HPC and Data Science Summer Institute will be limited to 45 candidates. Applicants will be screened to make sure that they have the necessary background to benefit from the institute, and that the applicants have a compelling need for the skills being taught in their research or teaching.

Program highlights

Preparation Day (Virtual – July 23, 2026)

Orientation: Logging into the Expanse Supercomputer, Linux/Unix basics, and running supercomputing jobs.

Summer institute:

Monday & Tuesday Morning - Foundational Skills

Data Management: File systems, compression, checksums, and secure transfer tools (wget, curl).

Batch Job Workflows: Writing Slurm scripts, job scheduling, and troubleshooting submissions.

Parallel Computing Basics: Scalability principles, processes vs threads, resource allocation, and benchmarking.

High-Throughput Computing: Strategies for managing a large number of small jobs for example for large-scale parameter sweeps.

Tuesday afternoon to Friday morning - Advanced techniques:

Parallel Computing using MPI & Open MP: Learn how to leverage all cores in a machine with OpenMP and how to scale your computations across multiple nodes with MPI using C and FORTRAN.

Performance Tuning: Cache optimization, loop-level parallelization, and compiler limitations.

GPU Programming: Understand GPU architecture, learn how to program GPUs with libraries, OpenACC directives and CUDA.

Deep Learning: Covers core neural network concepts and hands-on Keras implementation, progressing to advanced architectures (e.g., deep CNNs, transfer learning) and GPU-accelerated model optimization, with applications in image/speech recognition and biomedical domains.

Python for HPC: Easily speed-up Python on a single machine with numba and then scale a fully distributed workload on a cluster with dask.


See How To Apply for further details and to apply. 

Application Process & Fees

No fee required to apply. Registration fees apply only if your application is accepted:

Academic, Student, Government, & Non-Profit Organizations: $350

Industry Participants: $600

As experimental methods continue to evolve, generating increasingly complex and high-resolution datasets, machine learning (ML) is becoming an essential tool across numerous scientific disciplines. This conference will explore emerging ML methods and their applications in scientific discovery, focusing on processing technologies and strategies to accelerate deep learning and inference.
Fee Waivers: Thanks to generous support from our sponsors, the NSF HDR Institute AI-Accelerated Algorithms for Data-Driven Discovery (A3D3) and Exploring Neural Network Processors for AI in Science and Engineering (Voyager), we are offering in-person registration fee waivers (subject to funding availability) for graduate students on a first come first serve basis. If funds are still available, we will also open up fee waivers to undergraduate students and early-career researchers (<7 years since PhD) with financial need. Please indicate in the registration form if you are seeking a registration fee waiver and do not pay the fee.
Important Deadlines

• Abstract Submission: June 1, 2026
• (Optional) Extended Abstract Submission: June 15, 2026
• Early Bird Registration: July 15, 2026
  
  Topics include, but are not limited to:
  - Machine Learning Algorithm Design & Optimization
  - Accelerated Inference & Real-Time Processing
  - Scientific Applications of Fast ML
  - Scalable & Distributed ML Systems
  - Advanced Hardware & Computing Architectures 

We welcome abstracts for:

• Presentations and/or Posters
• • New for 2026: Submitters will have the option to submit a 4-page extended abstract (paper) to OpenReview (details to follow)
• Tutorials
• Topical (birds-of-a-feather) sessions

More information and registration details will follow. We look forward to welcoming you to San Diego this September!