NVIDIA's GPU Education Outreach Program enables classroom and lab use of NVIDIA technologies. Learn more about how NVIDIA plans to provide teaching materials, real GPU resources and software development tools for academic teaching faculty and system administrators world-wide. We will cover options available to give students access to GPU computing platforms, as well as how educators can access these systems and content. Additionally, we will discuss upcoming education outreach programs and seek feedback on how NVIDIA can help educators more easily teach massively parallel programming to their students or user base.
Learn how to use the CUDA computing platform as a tool to teach a wide array of parallel programming concepts. Examples will be given which demonstrate onboarding introductory students, to using the available tools to dive deep into complex parallel programming concepts. We'll also look at educators already using the CUDA platform the results they've attained. The world is parallel, and it's imperative we prepare students for the future. Recognizing this, the ACM and IEEE CS2013 curriculum guidelines second most important Key Area added was specifically about Parallel and Distributed Computing.
Starting with a background in C or C++, learn everything you need to know to accelerate your applications using CUDA C/C++. Beginning with a "Hello, World" CUDA C program, explore parallel programming with CUDA through a number of easy to follow code examples. Examine more deeply the various APIs available to CUDA applications and learn the best ways in which to employ them in your applications.
The rapid expansion of massively parallel computing, from smart phones to super computers, means we must improve and expand pedagogy in this field. CUDA is quickly becoming the go-to platform for teaching parallel programming at over 600 universities worldwide. Come join us at this session to hear from university faculty and industry professionals actively teaching CUDA across a wide spectrum of audiences. Learn what methods and materials work best for them. An "open-mic" Q&A session will follow brief presentations from each speaker, so come share your thoughts on the trends and needs of education for massively parallel computing.
Learn how to access the massively parallel processing power of NVIDIA GPUs using CUDA C and C++. Well start with a simple Hello Parallelism! program and progress on to something a little more complicated. You will see what actually happens when you compile & run and how to add GPU+CPU hybrid computing concepts to accelerate your applications.
How many ways can you program a GPU? This tutorial covers four practical methods of adding GPU acceleration to your applications. Take advantage of pre-packaged acceleration by dropping in a GPU-accelerated library to replace MKL, IPP, FFTW, or other libraries you are already using. Automatically parallelize ëforí loops in your C or Fortran code using OpenACC directives. Use powerful, cross-platform algorithms and data structures from the Thrust library of C++ to target CUDA, TBB and OpenMP. Develop your own parallel applications and libraries using a programming language you already know like C, C++, Fortran and more.