PUMPS Summer School, July 7-11, 2014

The Barcelona Supercomputing Center (BSC) in association with Universitat Politecnica de Catalunya (UPC) has been awarded by NVIDIA as a CUDA Center of Excellence (CCOE). 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 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)
  • Local Organizer: Nacho Navarro (BSC and UPC)
  • Dates:
    • Applications due: May 18, 2014
    • Notification of acceptance: May 30, 2014
    • Summer school: July 7-11, 2014
  • Location: Barcelona Supercomputing Center, Computer Architecture Dept. at Universitat Politecnica de Catalunya, Barcelona, Spain
  • 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 Algorithmic Optimization Strategies
    • Dealing with Sparse and Dynamic data
    • Efficiency in Large Data Traversal
    • Reducing Output Interference
    • Controlling Load Imbalance and Divergence
    • Debugging and Profiling CUDA Code
    • Multi-GPU Execution
    • FORTRAN Interoperability and CUDA Libraries
    • 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.
    • Distinguished Teachers: Wen-mei Hwu (Sanders III-Advanced Micro Devices Endowed Chair in Electrical and Computer Engineering in the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign) and David Kirk (NVIDIA Fellow, former Chief Scientist, NVIDIA Corporation )
    • BSC and UPC: Rosa Badia, Xavier Martorell, Nacho Navarro
    • Teaching Assistants: Javier Cabezas, Abdul Dakkak, Marc Jorda, Pau Farre, Diego Marron, Judit Planas, Xavier Teruel, Guillermo Miranda, Sergi Mateo
  • 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. We expect our sponsors will cover academic applicants' marginal expenses such as meals. Applicants from non-academic institutions (companies) , please contact us at 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, OpenCL
  • 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.
    • Attendees will have access to the BSC Supercomputer MinoTauro (Cluster of 128 Bull B505 blades with 2 M2090 NVIDIA GPU cards each).
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