NHR-Tutorial: 3-day-workshop - From Zero to Multi-Node GPU Programming

This weekly workshop series is jointly organized by NHR@FAU, NHR@TUD and NVIDIA DLI. It covers the following DLI courses:

• Fundamentals of Accelerated Computing with CUDA C/C++
• Accelerating CUDA C++ Applications with Multiple GPUs
• Scaling CUDA C++ Applications to Multiple Nodes

Please indicate which parts you want to attend when registering.

Date and Time

The courses will be held online on September 18th, September 25th and October 2nd, from 9 am to 5 pm.

Prerequisites

A free NVIDIA developer account is required to access the course material. Please register before the training at https://learn.nvidia.com/join.

Part 1

• Basic C/C++ competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
• No previous knowledge of CUDA programming is assumed

Parts 2 and 3

• Successful attendance of Part 1 (Fundamentals of Accelerated Computing with CUDA C/C++) or equivalent experience implementing CUDA C/C++ applications, including
  • memory allocation, host-to-device and device-to-host memory transfers,
  • kernel launches, grid-stride loops, and
  • CUDA error handling. 
• Familiarity with the Linux command line.
• Experience using Makefiles to compile C/C++ code.

Learning Objectives

Day 1

At the conclusion of the workshop, participants will have an understanding of the fundamental tools and techniques for GPU- accelerating C/C++ applications with CUDA and be able to:

• Write code to be executed by a GPU accelerator
• Expose and express data and instruction-level parallelism in C/C++ applications using CUDA
• Utilize CUDA-managed memory and optimize memory migration using asynchronous prefetching
• Leverage command-line and visual profilers to guide your work
• Utilize concurrent streams for instruction-level parallelism
• Write GPU-accelerated CUDA C/C++ applications, or refactor existing CPU-only applications, using a profile-driven approach

Day 2

At the conclusion of the workshop, you will be able to:

• Use concurrent CUDA streams to overlap memory transfers with GPU computation,
• Utilize all GPUs on a single node to scale workloads across available GPUs,
• Combine the use of copy/ compute overlap with multiple GPUs, and
• Rely on the NVIDIA Nsight Systems timeline to observe improvement opportunities and the impact of the techniques covered in the workshop.

Day 3

At the conclusion of the workshop, you will be able to:

• Use several methods for writing multi-GPU CUDA C++ applications,
• Use a variety of multi-GPU communication patterns and understand their tradeoffs,
• Write portable, scalable CUDA code with the single-program multiple-data (SPMD) paradigm using CUDA-aware MPI and NVSHMEM,
• Improve multi-GPU SPMD code with NVSHMEM’s symmetric memory model and its ability to perform GPU-initiated data transfers, and
• Get practice with common multi-GPU coding paradigms like domain decomposition and halo exchanges.

Language

The courses will be held in English.

Instructors

Dr. Sebastian Kuckuk and Markus Velten, both certified NVIDIA DLI Ambassadors.

The course is co-organised by NHR@FAU, NHR@TUD and the NVIDIA Deep Learning Institute (DLI).

Prices and Eligibility

The course is open and free of charge for participants from academia from European Union (EU) member states and countries associated under Horizon 2020.

Withdrawal Policy

Please only register for the course if you are really going to attend. No-shows will be blacklisted and excluded from future events. If you want to withdraw your registration, please send an e-mail to .

Wait List

To be added to the wait list after the course has reached its maximum number of registrations send an e-mail to  with your name, university affiliation and days you want to attend.