BSC holds the fifth edition of the PUMPS Summer School

03 July 2014

The PUMPS 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.

The fifth edition of the Programming and Tuning Massively Parallel Systems summer school (PUMPS) 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)

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

 Date: Monday, 7 July, 2014 - 09:00 to Friday, 11 July, 2014 - 18:00

 

Objectives: 

  • Instructors Wen-mei Hwu (University of Illinois) and David B. Kirk (NVIDIA), co-authors of “Programming Massively Parallel Processors, A Hands-on Approach”, will provide students with knowledge and hands-on experience in developing applications software for many-core processors, such as general purpose graphics processing units (GPUs).
  • 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.

Agenda: 

  • Topics:
    The following is a list of some of the topics that will be covered during the course. The updated full program will soon be available

    • CUDA Parallel Execution Model
    • CUDA Performance Considerations
    • CUDA Algorithmic Optimization Strategies
    • Data Locality Issues
    • Dealing with Sparse and Dynamic data
    • Efficiency in Large Data Traversal
    • Reducing Output Interference
    • Debugging and Profiling CUDA Code
    • GMAC Runtime
    • Multi-GPU Execution
    • Introduction to OmpSs
    • OmpSs: Leveraging GPU/CUDA Programming
    • Hands-on Labs: CUDA Optimizations and OmpSs Programming

The programme is available here