FUNDAMENTALS OF ACCELERATED COMPUTING WITH CUDA C/C++ ONLINE TRAINING
April 4, 2022
Fundamentals of Accelerated Computing with CUDA C/C++ workshop hosted by NVIDIA DLI and the Earth Observation Research & Innovation Centre (EORIC).
This workshop teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA®. You’ll learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the workshop, you’ll have access to additional resources to create new GPU-accelerated applications on your own.
At the conclusion of the workshop, you’ll 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
Why Deep Learning Institute Hands-On Training?
Learn to build deep learning and accelerated computing applications for industries such as autonomous vehicles, finance, game development, healthcare, robotics, and more.
Obtain hands-on experience with the most widely used, industry-standard software, tools, and frameworks.
Gain real-world expertise through content designed in collaboration with industry leaders such as the Children’s Hospital of Los Angeles, Mayo Clinic, and PwC.
Earn an NVIDIA DLI certificate to demonstrate your subject matter competency and support career growth.
Access content anywhere, anytime with a fully configured, GPU-accelerated workstation in the cloud.
Date, Venue, Fee
Date: Date to be communicated soon
To get the most out of this lab you should already be able to:
Declare variables, write loops, and use if / else statements in C.
Define and invoke functions in C.
Allocate arrays in C.
No previous CUDA knowledge is required.
Deadline for Registration: Friday, 22nd April 2022
Click here to register (Only 20 Participants will be selected)