Slides: Programming GPUs with CUDA
Slides: New Features in CUDA
Date: October 20, 2015
Title: "Programming GPUs with CUDA".
Lecturer: Manuel Ujaldon, Nvidia CUDA Fellow.
Prerequisites: Basics of C programming and concepts of parallel processing
will help, but are not critical to follow the lectures.
Target audience: M.Sc. and Ph.D. students, researchers and developers.
Those with CUDA experience can join us during the afternoon for knowing
the latest innovations on GPU computing.
||The GPU hardware: Many-core developments
||CUDA programming: Threads, blocks, kernels, grids
||CUDA examples: VectorAdd, Stencils, ReverseArray, MatrixProduct
||Advanced features: Dynamic parallelism, Hyper-Q, low power, unified memory
Manuel Ujaldon is Prof. of Computer Architecture at the University of Malaga (Spain) and CUDA Fellow at Nvidia.
He worked in the 90's on parallelizing compilers, finishing his PhD in 1996 by developing
a data-parallel compiler for sparse matrix and irregular applications. Over this period,
he was part of the HPF and MPI Forums, working as post-doc in the CS Dept. of the
University of Maryland (USA).
Last decade he started working on the GPGPU movement early in 2003
using Cg, and wrote the first book in spanish about programming GPUs
for general purpose computing. He adopted CUDA when it was first released,
then focusing on image processing and biomedical applications.
Over the past five years, he has published more than 50 papers in journals
and international conferences in these two areas.
Dr. Ujaldon has been awarded as NVIDIA Academic Partnership 2008-2011,
NVIDIA Teaching Center since 2011, NVIDIA Research Center since 2012,
and finally CUDA Fellow. Over the past four years, he has taught around
60 courses on CUDA programming worldwide sponsored by Nvidia,
including more than 10 keynotes and tutorials in ACM/IEEE conferences.
For more information, you can visit:
- His web page at Computer Architecture Department, Univ. of Malaga
- His web page as Nvidia CUDA Fellow
- GPGPU Computing on Image Processing (features extraction, segmentation, classifiers)
- Biomedical Applications (large-scale image analysis, biomarkers, bio-inspired algorithms and genomic wide association studies)
- Evolutionary Computation (Ant Colony Optimization)