Programming and Tuning Massively Parallel Systems + Artificial Intelligence Online Summer School, 5-9 July 2021

Publish Date: Mar 29, 2021

Deadline: Apr 30, 2021

Event Dates: from Jul 05, 2021 12:00 to Jul 09, 2021 12:00

PUMPS+AI Summer School, 2021, July 5-9 (tentative)

The Barcelona Supercomputing Center (BSC) in association with Universitat Politècnica de Catalunya (UPC) currently offer a number of courses covering CUDA architecture and programming languages for parallel computing. Please contact us for possible collaborations.

The eleventh edition of the Programming and Tuning Massively Parallel Systems + Artificial Intelligence summer school (PUMPS+AI) is aimed at enriching the skills of researchers, graduate students and teachers with cutting-edge technique and hands-on experience in developing applications for many-core processors with massively parallel computing resources like GPU accelerators.

  • Summer School Co-Directors: Mateo Valero (BSC and UPC) and Wen-mei Hwu (University of Illinois at Urbana-Champaign / NVIDIA)
  • Local Organizers: Antonio J. Peña (Chair, BSC and UPC), Marc Jordà (BSC), Simon Garcia de Gonzalo (BSC)
  • Dates:
    • Applications due: April 30, 2021
      • Due to space limitations, early application is strongly recommended. You may also be suggested to attend an online prerequisite training on basic CUDA programming before joining PUMPS.
    • Notification of acceptance: May 31, 2021
    • Hackathon day: July 5 (tentative, only for selected applicants)
    • Summer school: July 6-9 (tentative)
  • Location: This year's edition will be online. The schedule will be EU afternoon/US morning.
  • Organized by:
    • Barcelona Supercomputing Center (BSC)
    • University of Illinois at Urbana-Champaign (University of Illinois)
    • Universitat Politècnica de Catalunya (UPC)
    • HiPEAC Network of Excellence (HiPEAC)
    • PUMPS is part of this year PRACE Advanced Training Centre program
  • The following is a list of some of the topics that will be covered during the course:
    • Deep Learning
    • High-level programming models (OpenACC, Python, and Mathematica on GPUs)
    • CUDA Algorithmic Optimization Strategies
    • Dealing with Sparse and Dynamic data
    • Efficiency in Large Data Traversal
    • Reducing Output Interference
    • Controlling Load Imbalance and Divergence
    • Acceleration of Collective Operations
    • Dynamic Parallelism and HyperQ
    • Debugging and Profiling CUDA Code
    • Multi-GPU Execution
    • Architecture Trends and Implications
    • Introduction to OmpSs and to the Paraver analysis tool
    • OmpSs: Leveraging GPU/CUDA Programming
    • Hands-on Labs: CUDA Optimizations on Scientific Codes; OmpSs Programming and Tuning
  • Instructors:
    • Distinguished Lecturers: Wen-mei Hwu (University of Illinois at Urbana-Champaign / NVIDIA)
    • Invited Lecturer: Juan Gómez-Luna (ETH Zurich)
    • BSC / UPC Lecturers: Antonio J. Peña, Xavier Martorell and Xavier Teruel
    • Teaching Assistants:
      • UIUC: Carl Pearson, Mert Hidayetoglu
      • BSC/UPC: Marc Jorda, Simon Garcia de Gonzalo
  • Hackathon:
    • Juan Gómez-Luna (ETH Zurich)
  • Prerequisites for the course are:
    • Basic CUDA knowledge is required to attend the course. Applicants that cannot certify their experience in CUDA programming will be asked to take a short on-line course covering the necessary introductory topics
    • C, C++, Java, or equivalent programming knowledge. Skills in parallel programming will be helpful

Preliminary Overview

  • Registration for the course is free for attendees from academia and public institutions. Please note that travel, lodging, and meals are not covered. Applicants from non-academic institutions (companies), please contact us by email at pumps at for sponsorship possibilities.
  • By the end of the summer school, participants will:
    • Be able to design algorithms (including deep learning / AI) that are suitable for accelerators.
    • Understand the most important architectural performance considerations for developing parallel applications.
    • Be exposed to computational thinking skills for accelerating applications in science and engineering.
    • Engage computing accelerators on science and engineering breakthroughs.
  • Programming Languages: CUDA, MPI, OmpSs, OpenACC
  • Hands-on Labs: Afternoon labs with teaching assistants for each audience/level.
    • Participants are expected to bring their own laptops to access the servers with GPU accelerators.
    • The afternoon lab sessions will provide hands-on experience with various languages and tools covered in the lectures and will comprise a brief introduction to the programming assignments, followed by independent work periods. Teaching assistants will be available in person and on the web to help with assignments.

For more information click "LINK TO ORIGINAL" below.

Further Official Information

Link to Original

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