9th Programming and Tuning Massively Parallel Systems summer school (PUMPS)@BSC - UPC

16 Jul 2018, 09:00 → 20 Jul 2018, 17:00 CET

Lectures and Hands-on labs: A5101 (A5 building, 1st floor, room 101)

The Barcelona Supercomputing Center (BSC) in association with Universitat Politecnica de Catalunya (UPC) has been awarded by NVIDIA as a GPU Center of Excellence. BSC and UPC currently offer a number of courses covering CUDA architecture and programming languages for parallel computing. Please contact us for possible collaborations.

The ninth 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)

• Local Organizers: Antonio J. Peña (responsible, BSC and UPC), and Pau Farre (BSC)

• Dates:

  • Applications due: May 31, 2018

    • 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: June 12, 2018

  • Hackathon day: 15 July (only for selected applicants)

  • Summer school: 16-20 July

• Location: Barcelona Supercomputing Center, Computer Architecture Dept. at Universitat Politecnica de Catalunya, Barcelona, Spain

• Organized by:

  • Barcelona Supercomputing Center (BSC)

  • University of Illinois at Urbana-Champaign (University of Illinois)

  • Universitat Politecnica 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) and David Kirk (NVIDIA Corporation )

  • Invited Lecturer: Juan Gómez-Luna (Universidad de Córdoba)

  • BSC / UPC Lecturers: Antonio J. Peña, Xavier Martorell and Xavier Teruel

  • Teaching Assistants:

    • UIUC: Carl Pearson, Simon Garcia de Gonzalo, Mert Hidayetoglu

    • BSC/UPC: Marc Jorda, Pau Farre, Ivan Martinez, Aimar Rodriguez

• Hackathon:

  • Juan Gómez-Luna (Universidad de Córdoba)

  • Pedro Valero (BSC)

• 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 at pumps@bsc.es for sponsorship possibilities.

• By the end of the summer school, participants will:

  • Be able to design algorithms 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

• 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.