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Register to the new PUMPS+AI Summer School! Register Now
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Find your HPC course at PUMPS+AI View the program
The 10th PUMPS+AI Summer School will be held on June 24-29, 2019
Go to the 2019's website here
PUMPS+AI Summer School, 2018, July 16-20
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.
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Summer School Co-Directors: Mateo Valero (BSC and UPC) and Wen-mei Hwu (University of Illinois at Urbana-Champaign)
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Local Organizers: Antonio J. Peña (responsible, BSC and UPC), and Pau Farre (BSC)
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Dates:
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Applications due: May 31, 2018
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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.
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Notification of acceptance: June 12, 2018
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Hackathon day: 15 July (only for selected applicants)
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Summer school: 16-20 July
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Location: Barcelona Supercomputing Center, Computer Architecture Dept. at Universitat Politecnica de Catalunya, Barcelona, Spain
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Organized by:
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Barcelona Supercomputing Center (BSC)
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University of Illinois at Urbana-Champaign (University of Illinois)
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Universitat Politecnica de Catalunya (UPC)
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HiPEAC Network of Excellence (HiPEAC)
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PUMPS is part of this year PRACE Advanced Training Centre program
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The following is a list of some of the topics that will be covered during the course:
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Deep Learning
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High-level programming models (OpenACC, Python, and Mathematica on GPUs)
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CUDA Algorithmic Optimization Strategies
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Dealing with Sparse and Dynamic data
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Efficiency in Large Data Traversal
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Reducing Output Interference
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Controlling Load Imbalance and Divergence
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Acceleration of Collective Operations
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Dynamic Parallelism and HyperQ
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Debugging and Profiling CUDA Code
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Multi-GPU Execution
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Architecture Trends and Implications
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Introduction to OmpSs and to the Paraver analysis tool
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OmpSs: Leveraging GPU/CUDA Programming
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Hands-on Labs: CUDA Optimizations on Scientific Codes; OmpSs Programming and Tuning
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Distinguished Lecturers: Wen-mei Hwu (University of Illinois at Urbana-Champaign) and David Kirk (NVIDIA Corporation)
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Invited Lecturer: Juan Gómez-Luna (ETH Zurich)
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Teaching Assistants:
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Student Ambassador: Aleksandra Pachalieva (Technical University of Munich)
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Hackathon:
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Juan Gómez-Luna (ETH Zurich)
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Pedro Valero (BSC)
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Prerequisites for the course are:
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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
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C, C++, Java, or equivalent programming knowledge. Skills in parallel programming will be helpful
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Preliminary Overview
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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 bsc.es for sponsorship possibilities.
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By the end of the summer school, participants will:
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Be able to design algorithms that are suitable for accelerators.
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Understand the most important architectural performance considerations for developing parallel applications.
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Be exposed to computational thinking skills for accelerating applications in science and engineering.
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Engage computing accelerators on science and engineering breakthroughs.
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Programming Languages: CUDA, MPI, OmpSs
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Hands-on Labs: Afternoon labs with teaching assistants for each audience/level.
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Participants are expected to bring their own laptops to access the servers with GPU accelerators.
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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.
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