NVIDIA DLI & EORIC-UENR presents Fundamentals of Accelerated Computing with CUDA C/C++ Workshop
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Fundamentals of Accelerated Computing with CUDA C/C++ workshop hosted by NVIDIA Deep Learning Institute 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: Monday, 28th October 2019 – Tuesday, 29th October 2019
- Time: 1 pm to 7 pm
- Venue: University of Energy & Natural Resources
- Fee: Free
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, 25th October 2019
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