We'll discuss techniques for using more than one GPU in an OpenACC program. We'll demonstrate how to address multiple devices, mixing OpenACC and OpenMP to manage multiple devices, and utilizing multiple devices with OpenACC and MPI.
Join us for the free Introduction to OpenACC course this month, October, 2016. The course is comprised of three instructor-led classes that include interactive lectures with dedicated Q&A sections and hands-on exercises. You’ll learn everything you need to start accelerating your code with OpenACC on GPUs and CPUs. The course will cover introduction on how to analyze and parallelize your code, as well as perform optimizations like managing data movements and utilizing multiple GPUs.
This panel will discuss the current state of GPU programming using compiler directives, such as OpenACC and OpenMP. This session is a forum for discussing both the successes and shortcomings of using compiler directives to program GPUs. The panel will include users, speakers from compiler and tools vendors, and representatives of open source efforts to support directives. Session participants are encouraged to participate in the discussions of this panel.
This webinar will serve as an introductory tutorial for anyone interested in accelerated computing using compiler directives. Participants will learn about OpenACC and a proven process for accelerating applications using compiler directives. No prior GPU or parallel programming experience is required to attend this webinar, but the ability to read and understand C, C++, and or Fortran code is needed.
OpenACC is an open programming standard for parallel computing on accelerators (including GPUs) using directives. It is designed to make the transformative power of heterogeneous computing systems available to the developer quickly and easily. In this tutorial you will learn how to add simple directives to your code to expose parallelism to the compiler, allowing it to efficiently map computation onto an accelerator automatically. OpenACC allows developers to make simple and portable code changes, enabling an easier migration to accelerated computing.