GPU Programming Models and their Combinations

Venue: Cordoba, SPAIN
Event date: April 21, 2017
Speaker: Antonio J. Peña
 
CONTENTS
 
The aim of these courses is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors.
 
MORE INFO
While OpenACC focuses on coding productivity and portability, CUDA enables extracting the maximum performance from NVIDIA GPUs. OmpSs, on the other hand, is a GPU-aware task-based programming model which may be combined with CUDA, and recently with OpenACC as well. Using OpenACC we will start benefiting from GPU computing, obtaining great coding productivity and nice performance improvements. We can next fine-tune the critical application parts developing CUDA kernels to hand-optimize the problem. OmpSs combined with either OpenACC or CUDA will enable seamless task parallelism leveraging all system devices.