15-17 April 2020
Cineca - ONLINE
CET timezone

This course gives an overview of the most relevant GPGPU computing techniques to accelerate computationally demanding tasks on HPC heterogeneous architectures based on GPUs.

The course will start with an architectural overview of modern GPU based heterogeneous architectures, focusing on its computing power versus data movement needs. The course will cover a high level (pragma-based) programming approach with OpenACC for a fast porting startup, and lower level approaches based on nVIDIA CUDA  programming languages for finer grained computational intensive tasks. . A particular attention will be given on performance tuning and techniques to overcome common data movement bottlenecks and patterns.

Skills:
By the end of the course, students will be able to:

  • understand the strengths and weaknesses of GPUs as accelerators
  • program GPU accelerated applications using both higher and lower level programming approaches
  • overcome problems and bottlenecks regarding data movement between host and device memories
  • make best use of independent execution queues for concurrent computing/data-movement operations

Target Audience:
Researchers and programmers interested in porting scientific applications or use efficient post-process and data-analysis techniques in modern heterogeneous HPC architectures.

Prerequisites:

A basic knowledge of C or Fortran is mandatory. Programming and Linux or Unix. A basic knowledge of any parallel programming technique/paradigm is recommended.

Teachers: Dr. L.Ferraro, Dr. S.Orlandini

 

Starts
Ends
CET
Cineca - ONLINE

THIS COURSE IS AT CAPACITY AND NO MORE SPOTS ARE AVAILABLE. THE EVENT WILL BE RECORED AND THE VIDEOS, WITH ALL MATERIAL, WILL BE MADE AVAILABLE HERE IN THE MATERIAL SECTION. 

DUE TO RECENT COVID-19 EMERGENCY PLAN, THE COURSE WILL BE HELD ONLINE. More technical info few days before the date of the course.

Registration
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