INSTITUTION NAME: University of Wisconsin–Madison

COURSE SUBJECT, NUMBER AND TITLE: TAC-HEP : GPU training module 

CREDITS: 3 credits equivalent

COURSE DESCRIPTION:

Introduction to GPU programming. Re-cap on the basics of C++, introduction to the CUDA and ALPAKA programming models.

REQUISITES:

  • Familiarity navigating through UNIX based OS. Familiarity with CLI. Elementary knowledge of C or C++.
  • Students need to set-up a Wisconsin computing account and have login access to one of the UW GPU nodes. Students will be provided instructions for doing so prior to the start of the training.

MEETING TIME AND LOCATION:

Zoom coordinates: 

https://cern.zoom.us/j/69712006717?pwd=c0pqUGZxbUlFNkVRSWxHc24yL21tdz09

Meeting ID: 697 1200 6717

GPU Module

Lectures: 

Tuesdays 9:00 – 10:00 AM (CT), 10:00 – 11:00 AM (EST), 16:00 – 17:00 (CERN) 

Wednesdays 11:00 – 12:00 PM (CT), 12:00 – 13:00 (EST), 18:00 – 19:00 (CERN) 

via zoom

Note: Subject to change in weeks 5-7 since some lectures will be given by guest lecturers who are experts in the field and may have time conflicts  

Lectures: Tuesdays and Wednesday: 9:00 – 10:00 AM (CT), 10:00 – 11:00 AM (EST), 16:00 – 17:00 PM (CERN) via zoom

INSTRUCTIONAL MODALITY:

Virtual via zoom. There will be a combination of lectures and hands-on training.

OFFICE HOURS:

INSTRUCTOR CONTACT INFO:

GPU module

Dr. Charis Kleio Koraka

charis.kleio.koraka@cern.ch

COURSE LEARNING OUTCOMES:

Develop an understanding of the differences between different hardware (CPUs / GPUs). Get familiar with their use cases in HEP and develop the ability to identify the ideal hardware accelerator for different HEP applications. Become familiar with the CUDA and ALPAKA programming models. Learn how to use and interpret the output of profiling tools.

COURSE OVERVIEW:

REQUIRED TEXTBOOK, SOFTWARE AND OTHER COURSE MATERIALS:

  • No required textbook
  • All software will be installed on the available machines

HOMEWORK AND OTHER ASSIGNMENTS:

GPU Module

Weekly assignments for the following weeks

Week 2 : C++ assignment

Week 3 : CUDA assignment

Week 4 : CUDA assignment

Weeks 5-7 : Project

GRADING:

  GPU module
Weekly assignments  30%
Final project 20%
Overall  50 %

COURSE SCHEDULE/CALENDAR

Deadlines:

GPU Module

  • Each weekly assignment is due Friday of the next week (i.e. Week 2 assignment is due end of Week 3 on February the 10th)
  • Final project is due end (by Sunday) of week 7, Friday March 10th.
  • Each weekly assignment is due Friday of the next week 
  • Final project is due end (by Sunday) of week 7, Friday April 30th.

TOPICS COVERED

GPU Module

Week 1

  • Introduction to the training module 
  • Description of the CPU 
  • Hardware accelerators : types and applications
  • Description of the GPU 
  • GPU vs CPU
  • Heterogeneous computing
  • Computing challenge in HEP
  • GPU applications in HEP

Week 2

  • Introduction to C++ 
    • Core syntax and types
    • Operators
    • Arrays, Pointers & References
    • Control instructions (if/switch,for/while loops etc.)
    • Compound data types (structs, enums etc.)
    • Functions
    • Scopes / namespaces
    • Object Orientation (objects and classes/ constructors & destructors/ inheritance )

Week 3

  • Introduction to CUDA
    • Shell commands to use with GPUs
    • Concept of parallelization
    • GPU jargon
    • CUDA core syntax
    • GPU memory hierarchy and basic memory management
    • Error handling

Week 4

  • Continue the introduction to CUDA
    • CUDA execution model
    • How threads / warp size map to GPU architecture
    • Synchronization at grid and block level
    • Memory access patterns and coalesced memory accesses
    • Static and dynamic shared memory / optimizing memory performance using tiling
    • Race conditions and atomic operations

Week 5 - Week 6 - Week 7

  • Work on assigned project
  • Follow some advanced topics → to be determined. Some examples are :
    • Profiling C++ and CUDA code 
    • Read and interpret profiler output
    • Portability libraries
    • Concurrency using non-default CUDA streams 

Additional Read (if time permit) :

-   Computation-centric Algorithms

-   Control centric algorithms

-   Integration of multiple programs