+\chapter*{Preface}
+
This book is intended to present the design of significant scientific
applications on GPUs. Scientific applications require more and more
-computational power in a large vaariety of fields: biology, physics,
-chemisty, phenomon model and prediction, simulation, mathematics, ...
+computational power in a large variety of fields: biology, physics,
+chemistry, phenomon model and prediction, simulation, mathematics, etc.
In order to be able to handle more complex applications, the use of
parallel architectures is the solution to decrease the execution
-times of theses applications. Using simulataneously many computing
-cores can significantly speed up the processing time.
+times of these applications. Using many computing
+cores simulataneously can significantly speed up the processing time.
-Nevertheless using parallel architectures is not so easy and has
-always required an endeavor to parallelize an application. Nowadays
-with general purpose graphics processing units (GPGPU), it is possible
-to use either general graphic cards or dedicated graphic cards to benefit from
-the computational power of all the cores available inside these
-cards. The NVidia company introduced CUDA in 2007 to unify the
-programming model to use their video card. CUDA is currently the most
-used environment for designing GPU applications although some
-alternatives are available, for example OpenCL. According to
-applications and the GPU considered, a speed up from 5 up to 50 or even more can be
-expected using a GPU instead of computing with a CPU.
+Nevertheless using parallel architectures is not so easy and has always required
+an endeavor to parallelize an application. Nowadays with general purpose
+graphics processing units (GPGPU), it is possible to use either general graphic
+cards or dedicated graphic cards to benefit from the computational power of all
+the cores available inside these cards. The NVIDIA company introduced Compute
+Unified Device Architecture (CUDA) in 2007 to unify the programming model to use
+their video card. CUDA is currently the most used environment for designing GPU
+applications although some alternatives are available, such as Open Computing
+Language (OpenCL). According to applications and the GPU considered, a speed up
+from 5 up to 50, or even more can be expected using a GPU over computing with a
+CPU.
The programming model of GPU is quite different from the one of
CPU. It is well adapted to data parallelism applications. Several
books present the CUDA programming models and multi-core applications
design. This book is only focused on scientific applications on GPUs. It
-contains 19 chapters gathered in 5 parts.
+contains 20 chapters gathered in 6 parts.
The first part presents the GPUs. The second part focuses on two
significant image processing applications on GPUs. Part three presents
two general methodologies for software development on GPUs. Part four
-describes three optmitization problems on GPUs. The fifth part, the
-longuest one, presents 7 numerical applications. Finally part six
-illustrates 3 other applications that are not included in the previous
+describes three optimization problems on GPUs. The fifth part, the
+longest one, presents seven numerical applications. Finally part six
+illustrates three other applications that are not included in the previous
parts.
Some codes presented in this book are available online on my webpage:
-http://members.femto-st.fr/raphael-couturier/gpu-book/.
+http://members.femto-st.fr/raphael-couturier/en/gpu-book/
