NVIDIA GPUs are among the world’s fastest and most efficient accelerators delivering world record performance on both HPC and AI workloads. NVIDIA provides optimized software stacks, development tools, and libraries through CUDA-X that can be leveraged directly through CUDA C/C++ programming or through high-level programming languages like OpenACC. These tools and methods form a mature development ecosystem that allows researchers to become productive quickly without any GPU programming experience. Whether you’re creating a new application or trying to speed up an existing one, these courses will help you learn the most efficient, effective path forward.
Day 1: October 27
Fundamentals of Accelerated Computing with CUDA C/C++
Course Description: The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:
- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging Nsight Systems to guide and check your work
Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to fast track accelerated applications.
Prerequisites: Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations.
Technologies: C/C++, CUDA
Day 2: October 28
Fundamentals of Accelerated Computing with OpenACC
Course Description: Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this course, you’ll learn:
- How to profile and optimize your CPU-only applications to identify hot spots for acceleration
- How to use OpenACC directives to GPU accelerate your codebase
- How to optimize data movement between the CPU and GPU accelerator
Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.
Prerequisites: Basic experience with C/C++
Technologies: OpenACC, C/C++
In partnership with NVIDIA this event will be offered at no-charge. Registration is required and is expected to fill quickly. This event will be held remotely. Connection details will be sent to registrants shortly before the event. For questions contact events@sdsc.edu.
INSTRUCTOR
Abe Stern, Solutions Architect
NVIDIA
Dr. Abraham Stern is a solutions architect with NVIDIA focused on higher education and research. Abe's interests lie at the intersection of scientific computing and machine learning, especially as applied to problems in the chemistry and materials science domain. Abe obtained his Ph.D. in computational chemistry from the University of South Florida. Previously, Abe was a postdoctoral scholar at the University of California, Irvine. Abe has co-authored 19 scientific publications which have been cited more than 800 times and has been featured twice on journal covers.
San Diego Supercomputer Center (SDSC) at UC San Diego
contact: events@sdsc.edu